{"name":"app.neotic.www/neotic","slug":"www-neotic","title":"Neotic","description":"AI agents create contextual in-app experiences, announcements, and triggers with Neotic.","url":"https://mcp.market/server/www-neotic","rating":null,"grade":"C","score":60,"certified":false,"status":"active","category":"other","tags":[],"presence":{"score":8,"stars":null,"forks":null,"downloads_week":null,"last_push_at":null,"license":null},"uptime":{"percent":100,"checks":20,"ok":20,"last_checked_at":"2026-09-24T05:36:05.569Z","last_ok_at":"2026-09-24T05:36:05.569Z","latency_ms":189},"claimed":false,"transport":"remote","callable_via_gateway":true,"default_price_micros":0,"repository":null,"website":"https://www.neotic.app","version":"1.0.0","remotes":[{"type":"streamable-http","url":"https://www.neotic.app/api/mcp"}],"packages":[],"tools":[{"name":"cognitive.allocate_compute","description":"Adaptive compute budgeting: select compute tier (Fast Path to Deep Deliberation) and timeout based on EVC.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure":{"additionalProperties":true,"title":"Task Structure","type":"object"},"stakes":{"default":"normal","title":"Stakes","type":"string"}},"required":["task_structure"],"title":"cognitive_allocate_computeArguments"}},{"name":"cognitive.analogical_transfer","description":"Transfer structural strategies across disparate domains via Structure-Mapping Engine (SME).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"source_task_structure_id":{"title":"Source Task Structure Id","type":"string"},"target_task_structure_id":{"title":"Target Task Structure Id","type":"string"},"strategy_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Strategy Id"}},"required":["source_task_structure_id","target_task_structure_id"],"title":"cognitive_analogical_transferArguments"}},{"name":"cognitive.analyze_communication","description":"Pragmatic communication: audit speech acts against Gricean maxims (Quality, Quantity, Relation, Manner) and detect deception.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"content":{"title":"Content","type":"string"},"sender_id":{"default":"agent_1","title":"Sender Id","type":"string"},"recipient_id":{"default":"all","title":"Recipient Id","type":"string"},"act_type":{"default":"assert","title":"Act Type","type":"string"},"claims":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Claims"},"speaker_beliefs":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Speaker Beliefs"},"context_goals":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Context Goals"}},"required":["content"],"title":"cognitive_analyze_communicationArguments"}},{"name":"cognitive.arbitrate_temporal_objectives","description":"Arbitrate short vs long term payoffs using hyperbolic vs exponential discounting and Ulysses pre-commitment contracts.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"short_term_option":{"additionalProperties":true,"title":"Short Term Option","type":"object"},"long_term_option":{"additionalProperties":true,"title":"Long Term Option","type":"object"},"k_hyperbolic":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"K Hyperbolic"},"gamma_exponential":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Gamma Exponential"},"register_commitment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Register Commitment"},"audit_action_switch":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Audit Action Switch"}},"required":["short_term_option","long_term_option"],"title":"cognitive_arbitrate_temporal_objectivesArguments"}},{"name":"cognitive.assess_competence","description":"Epistemic boundary awareness: classify task into KNOWN, KNOWN_UNKNOWN, or UNKNOWN_UNKNOWN (OOD) and track calibration.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure":{"additionalProperties":true,"title":"Task Structure","type":"object"},"actual_outcome":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Actual Outcome"}},"required":["task_structure"],"title":"cognitive_assess_competenceArguments"}},{"name":"cognitive.audit_evidence_graph","description":"Audit the evidence graph for a task before issuing final answers.\n\n    Rejects claims such as 'optimal', 'verified', or 'feasible' when their\n    evidence dependencies are incomplete.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"}},"required":["task_structure_id"],"title":"cognitive_audit_evidence_graphArguments"}},{"name":"cognitive.build_evidence_graph","description":"Build or update an Evidence-Carrying Cognitive Graph for a task structure and solution trace.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"solution_trace":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Solution Trace"},"claims":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Claims"}},"required":["task_structure_id"],"title":"cognitive_build_evidence_graphArguments"}},{"name":"cognitive.causal_analysis","description":"Distinguish causal effects (do-calculus) from spurious correlation via backdoor adjustment.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"edges":{"items":{"items":{"type":"string"},"type":"array"},"title":"Edges","type":"array"},"treatment":{"title":"Treatment","type":"string"},"outcome":{"title":"Outcome","type":"string"},"observations":{"items":{"additionalProperties":true,"type":"object"},"title":"Observations","type":"array"}},"required":["edges","treatment","outcome","observations"],"title":"cognitive_causal_analysisArguments"}},{"name":"cognitive.compile_invariant_lattice","description":"Compile a Dynamic Constraint Lattice (DCL) into algebraic boundaries, conservation laws, and reachability cones.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Data"},"initial_state":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Initial State"},"goal_conditions":{"anyOf":[{"additionalProperties":{"items":{"anyOf":[{"type":"number"},{"type":"null"}]},"type":"array"},"type":"object"},{"type":"null"}],"default":null,"title":"Goal Conditions"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"},"max_rate_of_change":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Max Rate Of Change"}},"title":"cognitive_compile_invariant_latticeArguments"}},{"name":"cognitive.compose_strategies","description":"Skill Composition: synthesize a composite multi-stage StrategyIR from primitive strategies.\n\n    Chains multiple specialized skills (e.g. Graph Coloring + Topological Sort + Allocation)\n    into a compound pipeline with explicit stage transitions and end-to-end verification.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_ids":{"items":{"type":"string"},"title":"Strategy Ids","type":"array"},"composite_name":{"title":"Composite Name","type":"string"},"description":{"default":"","title":"Description","type":"string"}},"required":["strategy_ids","composite_name"],"title":"cognitive_compose_strategiesArguments"}},{"name":"cognitive.compute_intrinsic_rewards","description":"Compute intrinsic drives: novelty & prediction surprise curiosity, empowerment (channel capacity), and learning progress.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"actual_state":{"additionalProperties":true,"title":"Actual State","type":"object"},"predicted_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Predicted State"},"reachable_states":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Reachable States"},"extrinsic_reward":{"default":0,"title":"Extrinsic Reward","type":"number"},"skill_name":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Skill Name"},"skill_success":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Skill Success"}},"required":["actual_state"],"title":"cognitive_compute_intrinsic_rewardsArguments"}},{"name":"cognitive.compute_lattice_signature","description":"Compute coordinate-free topological invariant signature of a lattice or task.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Data"}},"title":"cognitive_compute_lattice_signatureArguments"}},{"name":"cognitive.compute_number_theory","description":"Number theory: primality, factoring, extended GCD, Diophantine, modular inverse, CRT, combinatorics, Fibonacci.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"operation":{"title":"Operation","type":"string"},"n":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"N"},"k":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"K"},"a":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"A"},"b":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"B"},"c":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"C"},"m":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"M"},"remainders":{"anyOf":[{"items":{"type":"integer"},"type":"array"},{"type":"null"}],"default":null,"title":"Remainders"},"moduli":{"anyOf":[{"items":{"type":"integer"},"type":"array"},{"type":"null"}],"default":null,"title":"Moduli"}},"required":["operation"],"title":"cognitive_compute_number_theoryArguments"}},{"name":"cognitive.counterfactual_what_if","description":"Counterfactual engine: evaluate 'What if I had done X instead of Y at step t?' using Pearl's abduction-intervention-prediction.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"factual_trace":{"items":{"additionalProperties":true,"type":"object"},"title":"Factual Trace","type":"array"},"intervention_step":{"title":"Intervention Step","type":"integer"},"counterfactual_action":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"string"}],"title":"Counterfactual Action"},"dt":{"default":1,"title":"Dt","type":"number"}},"required":["factual_trace","intervention_step","counterfactual_action"],"title":"cognitive_counterfactual_what_ifArguments"}},{"name":"cognitive.create_simulated_environment","description":"Instantiate and initialize a simulated cognitive environment (spatial_commons, multi_agent_arena, sequential_puzzle).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"env_type":{"default":"spatial_commons","title":"Env Type","type":"string"},"env_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Env Id"},"parameters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Parameters"}},"title":"cognitive_create_simulated_environmentArguments"}},{"name":"cognitive.crucible_stress_test","description":"Subject candidate trajectories to adversarial algebraic perturbations to calculate Robustness Index (R) and project hardened paths.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"trajectory":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Trajectory"},"stress_amplitude":{"default":0.15,"title":"Stress Amplitude","type":"number"},"max_perturbations":{"default":24,"title":"Max Perturbations","type":"integer"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"}},"title":"cognitive_crucible_stress_testArguments"}},{"name":"cognitive.evaluate_claim_evidence","description":"Evaluate support status and confidence for an individual claim with evidence.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"claim":{"title":"Claim","type":"string"},"subject":{"default":"","title":"Subject","type":"string"},"object":{"default":"","title":"Object","type":"string"},"relation":{"default":"states","title":"Relation","type":"string"},"evidence":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Evidence"},"assumptions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Assumptions"},"dependencies":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Dependencies"},"invalidation_conditions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Invalidation Conditions"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"}},"required":["claim"],"title":"cognitive_evaluate_claim_evidenceArguments"}},{"name":"cognitive.evaluate_cooperation","description":"Multi-agent cooperation: analyze game payoff matrices, compute Nash/Pareto equilibria, and execute reciprocity policies.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"game_type":{"title":"Game Type","type":"string"},"strategy":{"default":"tit_for_tat","title":"Strategy","type":"string"},"my_history":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"My History"},"partner_history":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Partner History"},"endowments":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Endowments"},"contributions":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Contributions"},"multiplier":{"default":1.6,"title":"Multiplier","type":"number"}},"required":["game_type"],"title":"cognitive_evaluate_cooperationArguments"}},{"name":"cognitive.evaluate_counterfactual_query","description":"Evaluate a counterfactual query on a plan ('What if capacity drops?', 'What if a route fails?', etc.).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"query_type":{"title":"Query Type","type":"string"},"parameters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Parameters"},"plan_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Plan Id"}},"required":["task_structure_id","query_type"],"title":"cognitive_evaluate_counterfactual_queryArguments"}},{"name":"cognitive.evaluate_generalization_benchmarks","description":"Evaluate broad generalization across spatial commons, multi-agent arenas, and sequential causal puzzles.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"benchmark_filter":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Benchmark Filter"}},"title":"cognitive_evaluate_generalization_benchmarksArguments"}},{"name":"cognitive.execute_task","description":"One-call orchestration: identify → gate → guide → solve → verify → report.\n\n    Parameters:\n    - task: Dict containing:\n      - task_structure (or loose definition: name, entities, constraints, etc.)\n      - raw (optional): Domain-specific execution payload. If omitted, returns\n        status='guidance_only' with 'recommended_action'='supply_raw' and\n        an 'expected_raw_formats' object detailing valid schemas.\n        Supported problem types for task.raw:\n        * scheduling: {\"workers\": [{\"id\": \"w1\", \"eligible_shifts\": [\"s1\"], \"max_shifts\": 1}],\n                       \"shifts\": [{\"id\": \"s1\", \"required_workers\": 1}]}\n        * allocation: {\"consumers\": [{\"id\": \"c1\", \"demands\": {\"r1\": 1}}],\n                       \"resources\": [{\"id\": \"r1\", \"capacity\": 2}]}\n        * graph: {\"nodes\": [\"A\", \"B\"], \"edges\": [[\"A\", \"B\"]]}\n        * graph_coloring: {\"nodes\": [\"A\", \"B\"], \"edges\": [[\"A\", \"B\"]], \"colors\": [\"red\", \"blue\"]}\n        * shortest_path: {\"nodes\": [\"A\", \"B\"], \"edges\": [[\"A\", \"B\"]], \"weights\": {\"A->B\": 1.0}, \"start\": \"A\", \"target\": \"B\"}\n        * math: {\"math\": {\"question\": \"...\", \"quantities\": {...}, \"equations\": [...], \"target_variable\": \"x\", \"ground_truth\": 42.0}}\n        * code: {\"code\": {\"code\": \"def solution()...\", \"tests\": [\"assert ...\"]}}\n        * pddl: {\"pddl\": {\"plan\": [...], \"init\": {...}, \"goal\": {...}}}\n\n    Returns a single envelope with status (completed / guidance_only /\n    blocked_until_clarified / no_applicable_guidance / refused_infeasible /\n    failed), solution, score, assumptions, failure reasons, and expected_raw_formats.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"task":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task"},"environment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Environment"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"},"model_family":{"default":"generic","title":"Model Family","type":"string"}},"title":"cognitive_execute_taskArguments"}},{"name":"cognitive.few_shot_induce","description":"Few-shot learning: induce a generalized procedural StrategyIR from 1-3 problem traces.\n\n    Extracts structural invariants (decision ordering, invariant contracts, verification rules)\n    and registers an initial candidate strategy immediately without requiring large training sets.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"solution_trace":{"additionalProperties":true,"title":"Solution Trace","type":"object"},"strategy_name":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Strategy Name"},"source_model":{"default":"few_shot_learner","title":"Source Model","type":"string"}},"required":["task_structure_id","solution_trace"],"title":"cognitive_few_shot_induceArguments"}},{"name":"cognitive.generate_and_prioritize_goals","description":"Autonomous goal synthesis from world deficits, depleted reserves, and exploration frontiers with multi-criteria prioritization.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"world_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"World State"},"unexplored_frontiers":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Unexplored Frontiers"},"depleted_reserves":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Depleted Reserves"},"max_active":{"default":3,"title":"Max Active","type":"integer"},"goal_status_update":{"anyOf":[{"additionalProperties":{"type":"string"},"type":"object"},{"type":"null"}],"default":null,"title":"Goal Status Update"}},"title":"cognitive_generate_and_prioritize_goalsArguments"}},{"name":"cognitive.get_experiment","description":"Retrieve details and benchmark results of an experiment (§24, §69).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"experiment_id":{"title":"Experiment Id","type":"string"}},"required":["experiment_id"],"title":"cognitive_get_experimentArguments"}},{"name":"cognitive.get_final_evidence_result","description":"Compile a final evidence result listing supporting evidence, assumptions, missing evidence,\n    contradictions, unchecked dependencies, confidence, and invalidation conditions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"target_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Id"}},"required":["task_structure_id"],"title":"cognitive_get_final_evidence_resultArguments"}},{"name":"cognitive.get_guidance","description":"Retrieve applicable validated strategies for a task (§24, §18).\n\n    Does NOT return unverified or suspended strategies as trusted guidance.\n    Provides calibrated uncertainty, applicability conditions, and negative transfer warnings.\n\n    Args:\n        task_structure_id: UUID of the abstract task structure.\n        environment: Environment characteristics.\n        goal: Goal description and metric targets.\n        available_capabilities: Capabilities supported by the caller.\n        model_family: Model family of the consumer agent (e.g. 'claude', 'gpt', 'local').\n\n    Returns:\n        Ranked list of applicable strategies with procedures, conditions, and evidence.\n        Failures return {\"error\", \"detail\", \"hint\"} — never a bare exception.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"environment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Environment"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"},"available_capabilities":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Available Capabilities"},"model_family":{"default":"generic","title":"Model Family","type":"string"}},"required":["task_structure_id"],"title":"cognitive_get_guidanceArguments"}},{"name":"cognitive.get_strategy","description":"Retrieve a usable strategy: steps, when to use, when not, evidence summary.\n\n    Disclosure: you learn WHAT to execute, never HOW the engine induces,\n    verifies, or ranks knowledge (no trust signals, audit, tenants, traces).\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"}},"required":["strategy_id"],"title":"cognitive_get_strategyArguments"}},{"name":"cognitive.get_strategy_report","description":"Evidence dossier: baseline vs assisted, distribution, CI, failures, last eval.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"}},"required":["strategy_id"],"title":"cognitive_get_strategy_reportArguments"}},{"name":"cognitive.ground_language","description":"Non-LLM compositional semantics: parse utterance into semantic predicates and evaluate directly against WorldState.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"utterance":{"title":"Utterance","type":"string"},"world_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"World State"}},"required":["utterance"],"title":"cognitive_ground_languageArguments"}},{"name":"cognitive.hierarchical_plan","description":"Decompose high-level goals into milestone subgoals using Hierarchical Task Network (HTN) planning.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"goal_tasks":{"items":{"type":"string"},"title":"Goal Tasks","type":"array"},"initial_state":{"additionalProperties":true,"title":"Initial State","type":"object"},"compound_tasks":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Compound Tasks"},"primitive_operators":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Primitive Operators"}},"required":["goal_tasks","initial_state"],"title":"cognitive_hierarchical_planArguments"}},{"name":"cognitive.identify_task","description":"Create or resolve an abstract task structure without storing raw private content (§24).\n\n    Args:\n        task_structure: Structural representation (entities, constraints, variables, etc.).\n        environment: Environmental context and characteristics.\n        goal: Objective and optimization goals.\n\n    Returns:\n        task_structure_id, structural_features, and matching existing structures.\n        On invalid input returns {\"error\", \"detail\", \"hint\"} instead of raising,\n        so the MCP client sees the cause instead of a generic execution error.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Structure"},"environment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Environment"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"}},"title":"cognitive_identify_taskArguments"}},{"name":"cognitive.induce_morphic_transfer","description":"Discover topological homomorphism between source experience and target problem, transducing solution paths.","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"source_lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Source Lattice Id"},"target_lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Lattice Id"},"target_task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Target Task Data"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"}},"title":"cognitive_induce_morphic_transferArguments"}},{"name":"cognitive.infer","description":"Perform logical deduction (Horn clauses) or exact probabilistic Bayesian network inference.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"facts":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Facts"},"rules":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Rules"},"query":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Query"},"probabilistic":{"default":false,"title":"Probabilistic","type":"boolean"},"query_var":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Query Var"},"evidence":{"anyOf":[{"additionalProperties":{"type":"string"},"type":"object"},{"type":"null"}],"default":null,"title":"Evidence"},"nodes":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Nodes"}},"title":"cognitive_inferArguments"}},{"name":"cognitive.infer_human_values","description":"Infer human values via Bradley-Terry IRL, detect Goodhart's law / specification gaming, and assess CIRL deference.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"comparisons":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Comparisons"},"detect_gaming":{"default":false,"title":"Detect Gaming","type":"boolean"},"proxy_metric":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Proxy Metric"},"baseline_metrics":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Baseline Metrics"},"projected_metrics":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Projected Metrics"},"action_evaluated":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Action Evaluated"},"action_stakes":{"default":"normal","title":"Action Stakes","type":"string"},"action_irreversible":{"default":false,"title":"Action Irreversible","type":"boolean"}},"title":"cognitive_infer_human_valuesArguments"}},{"name":"cognitive.inspect_lexicon","description":"Inspect grounded lexicon acquired via situated interaction (learned vocabulary, concept bindings, confidence).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"cognitive_inspect_lexiconArguments"}},{"name":"cognitive.inspect_self_model","description":"Engine self-model introspection: inspect capabilities, domain competence, active subsystems, and safety status.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"cognitive_inspect_self_modelArguments"}},{"name":"cognitive.learn_from_mistake","description":"Online Real-Time Error Reflection & Strategy Patching.\n\n    When an execution fails, analyzes root-cause constraint violations, synthesizes\n    new exception cases and repair procedures, verifies update against anchor regression,\n    and publishes the patched strategy version in real time.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"},"task_instance":{"additionalProperties":true,"title":"Task Instance","type":"object"},"execution_trace":{"additionalProperties":true,"title":"Execution Trace","type":"object"},"violations":{"items":{"type":"string"},"title":"Violations","type":"array"}},"required":["strategy_id","task_instance","execution_trace","violations"],"title":"cognitive_learn_from_mistakeArguments"}},{"name":"cognitive.learn_language_interaction","description":"Interactive language acquisition: learn word-concept bindings through cross-situational observation, games, and feedback.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"interaction_type":{"title":"Interaction Type","type":"string"},"utterance":{"title":"Utterance","type":"string"},"candidate_objects":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Candidate Objects"},"target_object_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Object Id"},"referent_features":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Referent Features"},"feedback_correct":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Feedback Correct"},"feedback_incorrect":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Feedback Incorrect"}},"required":["interaction_type","utterance"],"title":"cognitive_learn_language_interactionArguments"}},{"name":"cognitive.learn_world_model","description":"Online world model learning: update state transition priors from empirical execution traces.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"transitions":{"items":{"additionalProperties":true,"type":"object"},"title":"Transitions","type":"array"}},"required":["transitions"],"title":"cognitive_learn_world_modelArguments"}},{"name":"cognitive.list_experiments","description":"Discovery: list recorded benchmark experiment IDs for cognitive.get_experiment.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"cognitive_list_experimentsArguments"}},{"name":"cognitive.matrix_algebra","description":"Exact rational matrix & vector algebra: multiply, determinant, inverse, transpose, trace, eigenvalues, dot/cross.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"operation":{"title":"Operation","type":"string"},"matrix_A":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix A"},"matrix_B":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix B"},"vector_u":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"null"}],"default":null,"title":"Vector U"},"vector_v":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"null"}],"default":null,"title":"Vector V"}},"required":["operation"],"title":"cognitive_matrix_algebraArguments"}},{"name":"cognitive.monitor_reasoning","description":"Introspective reasoning critic: inspect trace in-flight to catch cycles, invariant drift, vacuous output, and stalling.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"trace_history":{"items":{"additionalProperties":true,"type":"object"},"title":"Trace History","type":"array"},"current_step":{"additionalProperties":true,"title":"Current Step","type":"object"},"invariants":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Invariants"}},"required":["trace_history","current_step"],"title":"cognitive_monitor_reasoningArguments"}},{"name":"cognitive.parse_task","description":"Convert natural-language task text into CIR and task_structure dict.\n\n    Every natural-language input is normalized into CIR before reasoning.\n    Returns both the normalized CIR and a human-readable explanation.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"text":{"default":"","title":"Text","type":"string"},"task_text":{"default":"","title":"Task Text","type":"string"},"description":{"default":"","title":"Description","type":"string"},"prompt":{"default":"","title":"Prompt","type":"string"}},"title":"cognitive_parse_taskArguments"}},{"name":"cognitive.plan_with_counterfactuals","description":"Synthesize the best verified plan across candidate rollouts, uncertainty tracking, and constraint pruning.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"current_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Current State"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"},"horizon":{"default":5,"title":"Horizon","type":"integer"},"method":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Method"}},"required":["task_structure_id"],"title":"cognitive_plan_with_counterfactualsArguments"}},{"name":"cognitive.predict_world_state","description":"Forward world model: predict future state trajectories and uncertainty bounds under actions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"state":{"additionalProperties":true,"title":"State","type":"object"},"actions":{"items":{},"title":"Actions","type":"array"},"timescale":{"default":"micro","title":"Timescale","type":"string"},"dt":{"default":1,"title":"Dt","type":"number"}},"required":["state","actions"],"title":"cognitive_predict_world_stateArguments"}},{"name":"cognitive.project_to_manifold","description":"Project a candidate state or plan step onto the Singular Transition Manifold M = F(S0) ∩ B(Goal).\n    \n    Returns the corrected state, corrective delta vector Delta S = S* - S, and boundary distance margins.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"State"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"}},"title":"cognitive_project_to_manifoldArguments"}},{"name":"cognitive.propose_strategy","description":"Propose a candidate strategy from problem-solving experience (§24, §2).\n\n    IMPORTANT: This NEVER makes the strategy TRUSTED.\n    The strategy enters CANDIDATE state and requires objective verification.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"name":{"title":"Name","type":"string"},"description":{"title":"Description","type":"string"},"procedure":{"items":{"type":"string"},"title":"Procedure","type":"array"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"experience_ids":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Experience Ids"},"preconditions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Preconditions"},"exceptions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Exceptions"},"applicability":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Applicability"},"source_model":{"default":"external_model","title":"Source Model","type":"string"}},"required":["name","description","procedure"],"title":"cognitive_propose_strategyArguments"}},{"name":"cognitive.record_experience","description":"Record an observable event in an ongoing experience episode (§24, §7).\n\n    Accepts structured actions, observations, and state changes.\n    Never sends raw unredacted private transcripts.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"experience_id":{"title":"Experience Id","type":"string"},"event_type":{"title":"Event Type","type":"string"},"event_data":{"additionalProperties":true,"title":"Event Data","type":"object"}},"required":["experience_id","event_type","event_data"],"title":"cognitive_record_experienceArguments"}},{"name":"cognitive.refine_lattice_from_feedback","description":"Autonomously evolve higher-order invariants, tighten bounds, and discover cliques from execution feedback.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"feedback_traces":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Feedback Traces"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"},"default_rate":{"default":1,"title":"Default Rate","type":"number"}},"title":"cognitive_refine_lattice_from_feedbackArguments"}},{"name":"cognitive.report_transfer","description":"Record whether a transferred strategy helped or harmed on a novel task (§24, §19).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"},"source_task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Source Task Structure Id"},"target_task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Task Structure Id"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"baseline_score":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Baseline Score"},"baseline_performance":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Baseline Performance"},"with_strategy_score":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"With Strategy Score"},"performance_with_strategy":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Performance With Strategy"},"consumer_model_family":{"default":"","title":"Consumer Model Family","type":"string"},"model_family":{"default":"","title":"Model Family","type":"string"},"transfer_type":{"default":"same_structure","title":"Transfer Type","type":"string"},"success":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Success"}},"required":["strategy_id"],"title":"cognitive_report_transferArguments"}},{"name":"cognitive.resolve_intent","description":"Pragmatics: resolve indirect speech acts (e.g. ability questions to directives), anaphoric pronouns, and verify presuppositions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"utterance":{"title":"Utterance","type":"string"},"speaker_id":{"default":"human","title":"Speaker Id","type":"string"},"world_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"World State"}},"required":["utterance"],"title":"cognitive_resolve_intentArguments"}},{"name":"cognitive.run_closed_loop_agent","description":"Run the end-to-end cognitive agent closed loop (Perceive -> Model -> Decide -> Act -> Reflect -> Learn).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"env_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Env Id"},"env_type":{"default":"spatial_commons","title":"Env Type","type":"string"},"max_steps":{"default":20,"title":"Max Steps","type":"integer"},"actions":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Actions"}},"title":"cognitive_run_closed_loop_agentArguments"}},{"name":"cognitive.run_multi_agent_simulation","description":"Simulate repeated multi-agent interaction with social dilemmas, speech acts, and reputation tracking.","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"game_type":{"default":"prisoners_dilemma","title":"Game Type","type":"string"},"opponent_policy":{"default":"tit_for_tat","title":"Opponent Policy","type":"string"},"num_rounds":{"default":10,"title":"Num Rounds","type":"integer"},"agent_actions":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Agent Actions"}},"title":"cognitive_run_multi_agent_simulationArguments"}},{"name":"cognitive.safe_self_improve","description":"Safe self-improvement: propose modifications guarded by immutable verification oracles and anchor regressions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"target_component":{"title":"Target Component","type":"string"},"patch_name":{"title":"Patch Name","type":"string"},"proposed_changes":{"additionalProperties":true,"title":"Proposed Changes","type":"object"},"rollback":{"default":false,"title":"Rollback","type":"boolean"},"rollback_snapshot_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Rollback Snapshot Id"},"simulated_regression_fail":{"default":false,"title":"Simulated Regression Fail","type":"boolean"}},"required":["target_component","patch_name","proposed_changes"],"title":"cognitive_safe_self_improveArguments"}},{"name":"cognitive.simulate_actions","description":"Simulate and rank candidate actions by predicted feasibility, reward, and constraint safety.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"state":{"additionalProperties":true,"title":"State","type":"object"},"candidate_actions":{"items":{},"title":"Candidate Actions","type":"array"},"dt":{"default":1,"title":"Dt","type":"number"}},"required":["state","candidate_actions"],"title":"cognitive_simulate_actionsArguments"}},{"name":"cognitive.solve_and_compare","description":"End-to-end autonomy: identify → guide → execute → baseline → verify → verdict.\n\n    Give raw task data (scheduling: workers/shifts/eligibility/capacity/\n    exclusivity; graph: nodes/edges; allocation: consumers/resources/...).\n    Returns the guided solution, the unguided baseline, independent\n    verification of both (with objective_source + independently_verified),\n    and whether the engine improved the result.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task"},"model_family":{"default":"generic","title":"Model Family","type":"string"}},"title":"cognitive_solve_and_compareArguments"}},{"name":"cognitive.solve_arithmetic","description":"Evaluate or simplify mathematical expressions (PEMDAS with power, functions like sqrt, exp, log, sin, cos).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"expression":{"title":"Expression","type":"string"},"variables":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Variables"},"simplify":{"default":false,"title":"Simplify","type":"boolean"}},"required":["expression"],"title":"cognitive_solve_arithmeticArguments"}},{"name":"cognitive.solve_equation_system","description":"Solve linear equations (ax + b = c), quadratic equations (ax^2 + bx + c = 0), or linear systems (A x = b).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"equation_type":{"title":"Equation Type","type":"string"},"linear_a":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Linear A"},"linear_b":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Linear B"},"linear_c":{"anyOf":[{"type":"number"},{"type":"null"}],"default":0,"title":"Linear C"},"quad_a":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Quad A"},"quad_b":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Quad B"},"quad_c":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Quad C"},"matrix_A":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix A"},"vector_b":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"null"}],"default":null,"title":"Vector B"}},"required":["equation_type"],"title":"cognitive_solve_equation_systemArguments"}},{"name":"cognitive.solve_word_problem","description":"Solve math word problems (GSM8K/SVAMP/MATH) via topological constraint propagation.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"question":{"default":"","title":"Question","type":"string"},"quantities":{"anyOf":[{"additionalProperties":{"anyOf":[{"type":"number"},{"type":"integer"}]},"type":"object"},{"type":"null"}],"default":null,"title":"Quantities"},"target_variable":{"default":"target","title":"Target Variable","type":"string"},"equations":{"anyOf":[{"items":{"additionalProperties":{"type":"string"},"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Equations"}},"title":"cognitive_solve_word_problemArguments"}},{"name":"cognitive.start_experience","description":"Start an experience episode (§24, §10).\n\n    Does not store raw prompts or full conversations. For long-horizon work,\n    pass parent_experience_id (+ subgoal) to chain episodes with an inherited\n    goal stack; unknown parents are rejected, never silently adopted.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"environment_id":{"title":"Environment Id","type":"string"},"agent_id":{"title":"Agent Id","type":"string"},"model_family":{"default":"generic","title":"Model Family","type":"string"},"model_version":{"default":"1.0","title":"Model Version","type":"string"},"task_instance_hash":{"default":"","title":"Task Instance Hash","type":"string"},"parent_experience_id":{"default":"","title":"Parent Experience Id","type":"string"},"subgoal":{"default":"","title":"Subgoal","type":"string"}},"required":["task_structure_id","environment_id","agent_id"],"title":"cognitive_start_experienceArguments"}},{"name":"cognitive.step_simulated_environment","description":"Step an active simulated environment with an agent action.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"env_id":{"title":"Env Id","type":"string"},"action":{"additionalProperties":true,"title":"Action","type":"object"}},"required":["env_id","action"],"title":"cognitive_step_simulated_environmentArguments"}},{"name":"cognitive.submit_outcome","description":"Submit the structured outcome of an experience episode (§24, §10).\n\n    Triggering this may induce candidate strategies in the engine.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"experience_id":{"title":"Experience Id","type":"string"},"result":{"additionalProperties":true,"title":"Result","type":"object"},"verifier_result":{"title":"Verifier Result","type":"string"},"metrics":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Metrics"},"failure_modes":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Failure Modes"},"success":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Success"}},"required":["experience_id","result","verifier_result"],"title":"cognitive_submit_outcomeArguments"}},{"name":"cognitive.synthesize_program","description":"Synthesize new algorithmic Python procedures on the fly with AST sandboxing and verification.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"problem_type":{"title":"Problem Type","type":"string"},"test_examples":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Test Examples"},"parameters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Parameters"}},"required":["problem_type"],"title":"cognitive_synthesize_programArguments"}},{"name":"cognitive.synthesize_singular_path","description":"Synthesize an optimal, invariant-verified trajectory from initial state to goal through the singular bottleneck.\n    \n    Eliminates dead-end branching and hallucinated unfeasible solutions.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Data"},"initial_state":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Initial State"},"goal_conditions":{"anyOf":[{"additionalProperties":{"items":{"anyOf":[{"type":"number"},{"type":"null"}]},"type":"array"},"type":"object"},{"type":"null"}],"default":null,"title":"Goal Conditions"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"}},"title":"cognitive_synthesize_singular_pathArguments"}},{"name":"cognitive.theory_of_mind","description":"Theory of Mind: model agents' BDI mental states, test false beliefs (Sally-Anne), and infer goals via inverse planning.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"agent_id":{"title":"Agent Id","type":"string"},"beliefs":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Beliefs"},"desires":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Desires"},"intentions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Intentions"},"action_trace":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Action Trace"},"candidate_goals":{"anyOf":[{"additionalProperties":{"items":{"type":"string"},"type":"array"},"type":"object"},{"type":"null"}],"default":null,"title":"Candidate Goals"},"evaluate_false_belief_fact":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Evaluate False Belief Fact"},"ground_truth":{"anyOf":[{},{"type":"null"}],"default":null,"title":"Ground Truth"},"witness_event":{"default":false,"title":"Witness Event","type":"boolean"}},"required":["agent_id"],"title":"cognitive_theory_of_mindArguments"}},{"name":"cognitive.tree_search","description":"Perform Monte Carlo Tree Search (UCT) over action sequences to find optimal trajectory.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"initial_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Initial State"},"valid_actions":{"anyOf":[{"items":{},"type":"array"},{"type":"null"}],"default":null,"title":"Valid Actions"},"max_iterations":{"default":100,"title":"Max Iterations","type":"integer"}},"title":"cognitive_tree_searchArguments"}},{"name":"cognitive.verify_arithmetic_claim","description":"Formally verify an arithmetic equality claim, audit numerical stability, condition number, and cancellation risks.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"claim_lhs":{"anyOf":[{"type":"string"},{"type":"number"}],"title":"Claim Lhs"},"claim_rhs":{"anyOf":[{"type":"string"},{"type":"number"}],"title":"Claim Rhs"},"tolerance":{"default":0.000001,"title":"Tolerance","type":"number"},"audit_stability":{"default":false,"title":"Audit Stability","type":"boolean"},"matrix":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix"}},"required":["claim_lhs","claim_rhs"],"title":"cognitive_verify_arithmetic_claimArguments"}},{"name":"cognitive.verify_ethics_and_norms","description":"Normative ethics & fairness: enforce deontological vetos, evaluate Rawlsian vs Utilitarian welfare, and arbitrate moral dilemmas.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"proposed_action_or_plan":{"anyOf":[{"additionalProperties":true,"type":"object"},{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Proposed Action Or Plan"},"stakeholder_payoffs":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Stakeholder Payoffs"},"options":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Options"}},"title":"cognitive_verify_ethics_and_normsArguments"}},{"name":"cognitive.verify_lattice_transition","description":"Verify a candidate state or transition S_t -> S_{t+1} against invariant boundary manifolds.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"},"prev_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Prev State"},"next_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Next State"},"action":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Action"}},"title":"cognitive_verify_lattice_transitionArguments"}},{"name":"cognitive.verify_strategy","description":"Run objective deterministic verification on a strategy (§24, §16).\n\n    Clients cannot self-promote. Verification is evaluated server-side.\n    Pass task_structure_id (from cognitive.identify_task) so constraints are\n    independently recomputed from registered descriptors instead of trusting\n    trace flags. Objective precedence: explicit caller value → recomputed from\n    raw data → registered spec (labeled unknown) → nested trace claims ONLY\n    when trust_trace_objective=true → otherwise unknown, never silent 0.0.\n    Returns passed/score plus details.objective_source and\n    details.independently_verified so callers know what was recomputed\n    versus taken on trace claims. Failures are structured, never bare.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"},"task_instance":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Instance"},"execution_trace":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Execution Trace"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"trust_trace_objective":{"default":false,"title":"Trust Trace Objective","type":"boolean"}},"required":["strategy_id"],"title":"cognitive_verify_strategyArguments"}},{"name":"cognitive_allocate_compute","description":"Adaptive compute budgeting: select compute tier (Fast Path to Deep Deliberation) and timeout based on EVC.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure":{"additionalProperties":true,"title":"Task Structure","type":"object"},"stakes":{"default":"normal","title":"Stakes","type":"string"}},"required":["task_structure"],"title":"cognitive_allocate_computeArguments"}},{"name":"cognitive_analogical_transfer","description":"Transfer structural strategies across disparate domains via Structure-Mapping Engine (SME).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"source_task_structure_id":{"title":"Source Task Structure Id","type":"string"},"target_task_structure_id":{"title":"Target Task Structure Id","type":"string"},"strategy_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Strategy Id"}},"required":["source_task_structure_id","target_task_structure_id"],"title":"cognitive_analogical_transferArguments"}},{"name":"cognitive_analyze_communication","description":"Pragmatic communication: audit speech acts against Gricean maxims (Quality, Quantity, Relation, Manner) and detect deception.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"content":{"title":"Content","type":"string"},"sender_id":{"default":"agent_1","title":"Sender Id","type":"string"},"recipient_id":{"default":"all","title":"Recipient Id","type":"string"},"act_type":{"default":"assert","title":"Act Type","type":"string"},"claims":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Claims"},"speaker_beliefs":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Speaker Beliefs"},"context_goals":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Context Goals"}},"required":["content"],"title":"cognitive_analyze_communicationArguments"}},{"name":"cognitive_arbitrate_temporal_objectives","description":"Arbitrate short vs long term payoffs using hyperbolic vs exponential discounting and Ulysses pre-commitment contracts.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"short_term_option":{"additionalProperties":true,"title":"Short Term Option","type":"object"},"long_term_option":{"additionalProperties":true,"title":"Long Term Option","type":"object"},"k_hyperbolic":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"K Hyperbolic"},"gamma_exponential":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Gamma Exponential"},"register_commitment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Register Commitment"},"audit_action_switch":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Audit Action Switch"}},"required":["short_term_option","long_term_option"],"title":"cognitive_arbitrate_temporal_objectivesArguments"}},{"name":"cognitive_assess_competence","description":"Epistemic boundary awareness: classify task into KNOWN, KNOWN_UNKNOWN, or UNKNOWN_UNKNOWN (OOD) and track calibration.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure":{"additionalProperties":true,"title":"Task Structure","type":"object"},"actual_outcome":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Actual Outcome"}},"required":["task_structure"],"title":"cognitive_assess_competenceArguments"}},{"name":"cognitive_audit_evidence_graph","description":"Audit the evidence graph for a task before issuing final answers.\n\n    Rejects claims such as 'optimal', 'verified', or 'feasible' when their\n    evidence dependencies are incomplete.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"}},"required":["task_structure_id"],"title":"cognitive_audit_evidence_graphArguments"}},{"name":"cognitive_build_evidence_graph","description":"Build or update an Evidence-Carrying Cognitive Graph for a task structure and solution trace.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"solution_trace":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Solution Trace"},"claims":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Claims"}},"required":["task_structure_id"],"title":"cognitive_build_evidence_graphArguments"}},{"name":"cognitive_causal_analysis","description":"Distinguish causal effects (do-calculus) from spurious correlation via backdoor adjustment.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"edges":{"items":{"items":{"type":"string"},"type":"array"},"title":"Edges","type":"array"},"treatment":{"title":"Treatment","type":"string"},"outcome":{"title":"Outcome","type":"string"},"observations":{"items":{"additionalProperties":true,"type":"object"},"title":"Observations","type":"array"}},"required":["edges","treatment","outcome","observations"],"title":"cognitive_causal_analysisArguments"}},{"name":"cognitive_compile_invariant_lattice","description":"Compile a Dynamic Constraint Lattice (DCL) into algebraic boundaries, conservation laws, and reachability cones.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Data"},"initial_state":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Initial State"},"goal_conditions":{"anyOf":[{"additionalProperties":{"items":{"anyOf":[{"type":"number"},{"type":"null"}]},"type":"array"},"type":"object"},{"type":"null"}],"default":null,"title":"Goal Conditions"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"},"max_rate_of_change":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Max Rate Of Change"}},"title":"cognitive_compile_invariant_latticeArguments"}},{"name":"cognitive_compose_strategies","description":"Skill Composition: synthesize a composite multi-stage StrategyIR from primitive strategies.\n\n    Chains multiple specialized skills (e.g. Graph Coloring + Topological Sort + Allocation)\n    into a compound pipeline with explicit stage transitions and end-to-end verification.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_ids":{"items":{"type":"string"},"title":"Strategy Ids","type":"array"},"composite_name":{"title":"Composite Name","type":"string"},"description":{"default":"","title":"Description","type":"string"}},"required":["strategy_ids","composite_name"],"title":"cognitive_compose_strategiesArguments"}},{"name":"cognitive_compute_intrinsic_rewards","description":"Compute intrinsic drives: novelty & prediction surprise curiosity, empowerment (channel capacity), and learning progress.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"actual_state":{"additionalProperties":true,"title":"Actual State","type":"object"},"predicted_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Predicted State"},"reachable_states":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Reachable States"},"extrinsic_reward":{"default":0,"title":"Extrinsic Reward","type":"number"},"skill_name":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Skill Name"},"skill_success":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Skill Success"}},"required":["actual_state"],"title":"cognitive_compute_intrinsic_rewardsArguments"}},{"name":"cognitive_compute_lattice_signature","description":"Compute coordinate-free topological invariant signature of a lattice or task.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Data"}},"title":"cognitive_compute_lattice_signatureArguments"}},{"name":"cognitive_compute_number_theory","description":"Number theory: primality, factoring, extended GCD, Diophantine, modular inverse, CRT, combinatorics, Fibonacci.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"operation":{"title":"Operation","type":"string"},"n":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"N"},"k":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"K"},"a":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"A"},"b":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"B"},"c":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"C"},"m":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"M"},"remainders":{"anyOf":[{"items":{"type":"integer"},"type":"array"},{"type":"null"}],"default":null,"title":"Remainders"},"moduli":{"anyOf":[{"items":{"type":"integer"},"type":"array"},{"type":"null"}],"default":null,"title":"Moduli"}},"required":["operation"],"title":"cognitive_compute_number_theoryArguments"}},{"name":"cognitive_counterfactual_what_if","description":"Counterfactual engine: evaluate 'What if I had done X instead of Y at step t?' using Pearl's abduction-intervention-prediction.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"factual_trace":{"items":{"additionalProperties":true,"type":"object"},"title":"Factual Trace","type":"array"},"intervention_step":{"title":"Intervention Step","type":"integer"},"counterfactual_action":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"string"}],"title":"Counterfactual Action"},"dt":{"default":1,"title":"Dt","type":"number"}},"required":["factual_trace","intervention_step","counterfactual_action"],"title":"cognitive_counterfactual_what_ifArguments"}},{"name":"cognitive_create_simulated_environment","description":"Instantiate and initialize a simulated cognitive environment (spatial_commons, multi_agent_arena, sequential_puzzle).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"env_type":{"default":"spatial_commons","title":"Env Type","type":"string"},"env_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Env Id"},"parameters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Parameters"}},"title":"cognitive_create_simulated_environmentArguments"}},{"name":"cognitive_crucible_stress_test","description":"Subject candidate trajectories to adversarial algebraic perturbations to calculate Robustness Index (R) and project hardened paths.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"trajectory":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Trajectory"},"stress_amplitude":{"default":0.15,"title":"Stress Amplitude","type":"number"},"max_perturbations":{"default":24,"title":"Max Perturbations","type":"integer"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"}},"title":"cognitive_crucible_stress_testArguments"}},{"name":"cognitive_evaluate_claim_evidence","description":"Evaluate support status and confidence for an individual claim with evidence.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"claim":{"title":"Claim","type":"string"},"subject":{"default":"","title":"Subject","type":"string"},"object":{"default":"","title":"Object","type":"string"},"relation":{"default":"states","title":"Relation","type":"string"},"evidence":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Evidence"},"assumptions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Assumptions"},"dependencies":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Dependencies"},"invalidation_conditions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Invalidation Conditions"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"}},"required":["claim"],"title":"cognitive_evaluate_claim_evidenceArguments"}},{"name":"cognitive_evaluate_cooperation","description":"Multi-agent cooperation: analyze game payoff matrices, compute Nash/Pareto equilibria, and execute reciprocity policies.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"game_type":{"title":"Game Type","type":"string"},"strategy":{"default":"tit_for_tat","title":"Strategy","type":"string"},"my_history":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"My History"},"partner_history":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Partner History"},"endowments":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Endowments"},"contributions":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Contributions"},"multiplier":{"default":1.6,"title":"Multiplier","type":"number"}},"required":["game_type"],"title":"cognitive_evaluate_cooperationArguments"}},{"name":"cognitive_evaluate_counterfactual_query","description":"Evaluate a counterfactual query on a plan ('What if capacity drops?', 'What if a route fails?', etc.).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"query_type":{"title":"Query Type","type":"string"},"parameters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Parameters"},"plan_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Plan Id"}},"required":["task_structure_id","query_type"],"title":"cognitive_evaluate_counterfactual_queryArguments"}},{"name":"cognitive_evaluate_generalization_benchmarks","description":"Evaluate broad generalization across spatial commons, multi-agent arenas, and sequential causal puzzles.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"benchmark_filter":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Benchmark Filter"}},"title":"cognitive_evaluate_generalization_benchmarksArguments"}},{"name":"cognitive_execute_task","description":"One-call orchestration: identify → gate → guide → solve → verify → report.\n\n    Parameters:\n    - task: Dict containing:\n      - task_structure (or loose definition: name, entities, constraints, etc.)\n      - raw (optional): Domain-specific execution payload. If omitted, returns\n        status='guidance_only' with 'recommended_action'='supply_raw' and\n        an 'expected_raw_formats' object detailing valid schemas.\n        Supported problem types for task.raw:\n        * scheduling: {\"workers\": [{\"id\": \"w1\", \"eligible_shifts\": [\"s1\"], \"max_shifts\": 1}],\n                       \"shifts\": [{\"id\": \"s1\", \"required_workers\": 1}]}\n        * allocation: {\"consumers\": [{\"id\": \"c1\", \"demands\": {\"r1\": 1}}],\n                       \"resources\": [{\"id\": \"r1\", \"capacity\": 2}]}\n        * graph: {\"nodes\": [\"A\", \"B\"], \"edges\": [[\"A\", \"B\"]]}\n        * graph_coloring: {\"nodes\": [\"A\", \"B\"], \"edges\": [[\"A\", \"B\"]], \"colors\": [\"red\", \"blue\"]}\n        * shortest_path: {\"nodes\": [\"A\", \"B\"], \"edges\": [[\"A\", \"B\"]], \"weights\": {\"A->B\": 1.0}, \"start\": \"A\", \"target\": \"B\"}\n        * math: {\"math\": {\"question\": \"...\", \"quantities\": {...}, \"equations\": [...], \"target_variable\": \"x\", \"ground_truth\": 42.0}}\n        * code: {\"code\": {\"code\": \"def solution()...\", \"tests\": [\"assert ...\"]}}\n        * pddl: {\"pddl\": {\"plan\": [...], \"init\": {...}, \"goal\": {...}}}\n\n    Returns a single envelope with status (completed / guidance_only /\n    blocked_until_clarified / no_applicable_guidance / refused_infeasible /\n    failed), solution, score, assumptions, failure reasons, and expected_raw_formats.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"task":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task"},"environment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Environment"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"},"model_family":{"default":"generic","title":"Model Family","type":"string"}},"title":"cognitive_execute_taskArguments"}},{"name":"cognitive_few_shot_induce","description":"Few-shot learning: induce a generalized procedural StrategyIR from 1-3 problem traces.\n\n    Extracts structural invariants (decision ordering, invariant contracts, verification rules)\n    and registers an initial candidate strategy immediately without requiring large training sets.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"solution_trace":{"additionalProperties":true,"title":"Solution Trace","type":"object"},"strategy_name":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Strategy Name"},"source_model":{"default":"few_shot_learner","title":"Source Model","type":"string"}},"required":["task_structure_id","solution_trace"],"title":"cognitive_few_shot_induceArguments"}},{"name":"cognitive_generate_and_prioritize_goals","description":"Autonomous goal synthesis from world deficits, depleted reserves, and exploration frontiers with multi-criteria prioritization.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"world_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"World State"},"unexplored_frontiers":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Unexplored Frontiers"},"depleted_reserves":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Depleted Reserves"},"max_active":{"default":3,"title":"Max Active","type":"integer"},"goal_status_update":{"anyOf":[{"additionalProperties":{"type":"string"},"type":"object"},{"type":"null"}],"default":null,"title":"Goal Status Update"}},"title":"cognitive_generate_and_prioritize_goalsArguments"}},{"name":"cognitive_get_experiment","description":"Retrieve details and benchmark results of an experiment (§24, §69).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"experiment_id":{"title":"Experiment Id","type":"string"}},"required":["experiment_id"],"title":"cognitive_get_experimentArguments"}},{"name":"cognitive_get_final_evidence_result","description":"Compile a final evidence result listing supporting evidence, assumptions, missing evidence,\n    contradictions, unchecked dependencies, confidence, and invalidation conditions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"target_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Id"}},"required":["task_structure_id"],"title":"cognitive_get_final_evidence_resultArguments"}},{"name":"cognitive_get_guidance","description":"Retrieve applicable validated strategies for a task (§24, §18).\n\n    Does NOT return unverified or suspended strategies as trusted guidance.\n    Provides calibrated uncertainty, applicability conditions, and negative transfer warnings.\n\n    Args:\n        task_structure_id: UUID of the abstract task structure.\n        environment: Environment characteristics.\n        goal: Goal description and metric targets.\n        available_capabilities: Capabilities supported by the caller.\n        model_family: Model family of the consumer agent (e.g. 'claude', 'gpt', 'local').\n\n    Returns:\n        Ranked list of applicable strategies with procedures, conditions, and evidence.\n        Failures return {\"error\", \"detail\", \"hint\"} — never a bare exception.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"environment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Environment"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"},"available_capabilities":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Available Capabilities"},"model_family":{"default":"generic","title":"Model Family","type":"string"}},"required":["task_structure_id"],"title":"cognitive_get_guidanceArguments"}},{"name":"cognitive_get_strategy","description":"Retrieve a usable strategy: steps, when to use, when not, evidence summary.\n\n    Disclosure: you learn WHAT to execute, never HOW the engine induces,\n    verifies, or ranks knowledge (no trust signals, audit, tenants, traces).\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"}},"required":["strategy_id"],"title":"cognitive_get_strategyArguments"}},{"name":"cognitive_get_strategy_report","description":"Evidence dossier: baseline vs assisted, distribution, CI, failures, last eval.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"}},"required":["strategy_id"],"title":"cognitive_get_strategy_reportArguments"}},{"name":"cognitive_ground_language","description":"Non-LLM compositional semantics: parse utterance into semantic predicates and evaluate directly against WorldState.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"utterance":{"title":"Utterance","type":"string"},"world_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"World State"}},"required":["utterance"],"title":"cognitive_ground_languageArguments"}},{"name":"cognitive_hierarchical_plan","description":"Decompose high-level goals into milestone subgoals using Hierarchical Task Network (HTN) planning.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"goal_tasks":{"items":{"type":"string"},"title":"Goal Tasks","type":"array"},"initial_state":{"additionalProperties":true,"title":"Initial State","type":"object"},"compound_tasks":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Compound Tasks"},"primitive_operators":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Primitive Operators"}},"required":["goal_tasks","initial_state"],"title":"cognitive_hierarchical_planArguments"}},{"name":"cognitive_identify_task","description":"Create or resolve an abstract task structure without storing raw private content (§24).\n\n    Args:\n        task_structure: Structural representation (entities, constraints, variables, etc.).\n        environment: Environmental context and characteristics.\n        goal: Objective and optimization goals.\n\n    Returns:\n        task_structure_id, structural_features, and matching existing structures.\n        On invalid input returns {\"error\", \"detail\", \"hint\"} instead of raising,\n        so the MCP client sees the cause instead of a generic execution error.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Structure"},"environment":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Environment"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"}},"title":"cognitive_identify_taskArguments"}},{"name":"cognitive_induce_morphic_transfer","description":"Discover topological homomorphism between source experience and target problem, transducing solution paths.","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"source_lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Source Lattice Id"},"target_lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Lattice Id"},"target_task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Target Task Data"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"}},"title":"cognitive_induce_morphic_transferArguments"}},{"name":"cognitive_infer","description":"Perform logical deduction (Horn clauses) or exact probabilistic Bayesian network inference.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"facts":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Facts"},"rules":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Rules"},"query":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Query"},"probabilistic":{"default":false,"title":"Probabilistic","type":"boolean"},"query_var":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Query Var"},"evidence":{"anyOf":[{"additionalProperties":{"type":"string"},"type":"object"},{"type":"null"}],"default":null,"title":"Evidence"},"nodes":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Nodes"}},"title":"cognitive_inferArguments"}},{"name":"cognitive_infer_human_values","description":"Infer human values via Bradley-Terry IRL, detect Goodhart's law / specification gaming, and assess CIRL deference.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"comparisons":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Comparisons"},"detect_gaming":{"default":false,"title":"Detect Gaming","type":"boolean"},"proxy_metric":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Proxy Metric"},"baseline_metrics":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Baseline Metrics"},"projected_metrics":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Projected Metrics"},"action_evaluated":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Action Evaluated"},"action_stakes":{"default":"normal","title":"Action Stakes","type":"string"},"action_irreversible":{"default":false,"title":"Action Irreversible","type":"boolean"}},"title":"cognitive_infer_human_valuesArguments"}},{"name":"cognitive_inspect_lexicon","description":"Inspect grounded lexicon acquired via situated interaction (learned vocabulary, concept bindings, confidence).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"cognitive_inspect_lexiconArguments"}},{"name":"cognitive_inspect_self_model","description":"Engine self-model introspection: inspect capabilities, domain competence, active subsystems, and safety status.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"cognitive_inspect_self_modelArguments"}},{"name":"cognitive_learn_from_mistake","description":"Online Real-Time Error Reflection & Strategy Patching.\n\n    When an execution fails, analyzes root-cause constraint violations, synthesizes\n    new exception cases and repair procedures, verifies update against anchor regression,\n    and publishes the patched strategy version in real time.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"},"task_instance":{"additionalProperties":true,"title":"Task Instance","type":"object"},"execution_trace":{"additionalProperties":true,"title":"Execution Trace","type":"object"},"violations":{"items":{"type":"string"},"title":"Violations","type":"array"}},"required":["strategy_id","task_instance","execution_trace","violations"],"title":"cognitive_learn_from_mistakeArguments"}},{"name":"cognitive_learn_language_interaction","description":"Interactive language acquisition: learn word-concept bindings through cross-situational observation, games, and feedback.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"interaction_type":{"title":"Interaction Type","type":"string"},"utterance":{"title":"Utterance","type":"string"},"candidate_objects":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Candidate Objects"},"target_object_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Object Id"},"referent_features":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Referent Features"},"feedback_correct":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Feedback Correct"},"feedback_incorrect":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Feedback Incorrect"}},"required":["interaction_type","utterance"],"title":"cognitive_learn_language_interactionArguments"}},{"name":"cognitive_learn_world_model","description":"Online world model learning: update state transition priors from empirical execution traces.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"transitions":{"items":{"additionalProperties":true,"type":"object"},"title":"Transitions","type":"array"}},"required":["transitions"],"title":"cognitive_learn_world_modelArguments"}},{"name":"cognitive_list_experiments","description":"Discovery: list recorded benchmark experiment IDs for cognitive.get_experiment.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"cognitive_list_experimentsArguments"}},{"name":"cognitive_matrix_algebra","description":"Exact rational matrix & vector algebra: multiply, determinant, inverse, transpose, trace, eigenvalues, dot/cross.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"operation":{"title":"Operation","type":"string"},"matrix_A":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix A"},"matrix_B":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix B"},"vector_u":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"null"}],"default":null,"title":"Vector U"},"vector_v":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"null"}],"default":null,"title":"Vector V"}},"required":["operation"],"title":"cognitive_matrix_algebraArguments"}},{"name":"cognitive_monitor_reasoning","description":"Introspective reasoning critic: inspect trace in-flight to catch cycles, invariant drift, vacuous output, and stalling.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"trace_history":{"items":{"additionalProperties":true,"type":"object"},"title":"Trace History","type":"array"},"current_step":{"additionalProperties":true,"title":"Current Step","type":"object"},"invariants":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Invariants"}},"required":["trace_history","current_step"],"title":"cognitive_monitor_reasoningArguments"}},{"name":"cognitive_parse_task","description":"Convert natural-language task text into CIR and task_structure dict.\n\n    Every natural-language input is normalized into CIR before reasoning.\n    Returns both the normalized CIR and a human-readable explanation.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"text":{"default":"","title":"Text","type":"string"},"task_text":{"default":"","title":"Task Text","type":"string"},"description":{"default":"","title":"Description","type":"string"},"prompt":{"default":"","title":"Prompt","type":"string"}},"title":"cognitive_parse_taskArguments"}},{"name":"cognitive_plan_with_counterfactuals","description":"Synthesize the best verified plan across candidate rollouts, uncertainty tracking, and constraint pruning.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"current_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Current State"},"goal":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Goal"},"horizon":{"default":5,"title":"Horizon","type":"integer"},"method":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Method"}},"required":["task_structure_id"],"title":"cognitive_plan_with_counterfactualsArguments"}},{"name":"cognitive_predict_world_state","description":"Forward world model: predict future state trajectories and uncertainty bounds under actions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"state":{"additionalProperties":true,"title":"State","type":"object"},"actions":{"items":{},"title":"Actions","type":"array"},"timescale":{"default":"micro","title":"Timescale","type":"string"},"dt":{"default":1,"title":"Dt","type":"number"}},"required":["state","actions"],"title":"cognitive_predict_world_stateArguments"}},{"name":"cognitive_project_to_manifold","description":"Project a candidate state or plan step onto the Singular Transition Manifold M = F(S0) ∩ B(Goal).\n    \n    Returns the corrected state, corrective delta vector Delta S = S* - S, and boundary distance margins.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"State"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"}},"title":"cognitive_project_to_manifoldArguments"}},{"name":"cognitive_propose_strategy","description":"Propose a candidate strategy from problem-solving experience (§24, §2).\n\n    IMPORTANT: This NEVER makes the strategy TRUSTED.\n    The strategy enters CANDIDATE state and requires objective verification.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"name":{"title":"Name","type":"string"},"description":{"title":"Description","type":"string"},"procedure":{"items":{"type":"string"},"title":"Procedure","type":"array"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"experience_ids":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Experience Ids"},"preconditions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Preconditions"},"exceptions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Exceptions"},"applicability":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Applicability"},"source_model":{"default":"external_model","title":"Source Model","type":"string"}},"required":["name","description","procedure"],"title":"cognitive_propose_strategyArguments"}},{"name":"cognitive_record_experience","description":"Record an observable event in an ongoing experience episode (§24, §7).\n\n    Accepts structured actions, observations, and state changes.\n    Never sends raw unredacted private transcripts.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"experience_id":{"title":"Experience Id","type":"string"},"event_type":{"title":"Event Type","type":"string"},"event_data":{"additionalProperties":true,"title":"Event Data","type":"object"}},"required":["experience_id","event_type","event_data"],"title":"cognitive_record_experienceArguments"}},{"name":"cognitive_refine_lattice_from_feedback","description":"Autonomously evolve higher-order invariants, tighten bounds, and discover cliques from execution feedback.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"feedback_traces":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Feedback Traces"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"},"default_rate":{"default":1,"title":"Default Rate","type":"number"}},"title":"cognitive_refine_lattice_from_feedbackArguments"}},{"name":"cognitive_report_transfer","description":"Record whether a transferred strategy helped or harmed on a novel task (§24, §19).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"},"source_task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Source Task Structure Id"},"target_task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Target Task Structure Id"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"baseline_score":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Baseline Score"},"baseline_performance":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Baseline Performance"},"with_strategy_score":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"With Strategy Score"},"performance_with_strategy":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Performance With Strategy"},"consumer_model_family":{"default":"","title":"Consumer Model Family","type":"string"},"model_family":{"default":"","title":"Model Family","type":"string"},"transfer_type":{"default":"same_structure","title":"Transfer Type","type":"string"},"success":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Success"}},"required":["strategy_id"],"title":"cognitive_report_transferArguments"}},{"name":"cognitive_resolve_intent","description":"Pragmatics: resolve indirect speech acts (e.g. ability questions to directives), anaphoric pronouns, and verify presuppositions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"utterance":{"title":"Utterance","type":"string"},"speaker_id":{"default":"human","title":"Speaker Id","type":"string"},"world_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"World State"}},"required":["utterance"],"title":"cognitive_resolve_intentArguments"}},{"name":"cognitive_run_closed_loop_agent","description":"Run the end-to-end cognitive agent closed loop (Perceive -> Model -> Decide -> Act -> Reflect -> Learn).","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"env_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Env Id"},"env_type":{"default":"spatial_commons","title":"Env Type","type":"string"},"max_steps":{"default":20,"title":"Max Steps","type":"integer"},"actions":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Actions"}},"title":"cognitive_run_closed_loop_agentArguments"}},{"name":"cognitive_run_multi_agent_simulation","description":"Simulate repeated multi-agent interaction with social dilemmas, speech acts, and reputation tracking.","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"game_type":{"default":"prisoners_dilemma","title":"Game Type","type":"string"},"opponent_policy":{"default":"tit_for_tat","title":"Opponent Policy","type":"string"},"num_rounds":{"default":10,"title":"Num Rounds","type":"integer"},"agent_actions":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Agent Actions"}},"title":"cognitive_run_multi_agent_simulationArguments"}},{"name":"cognitive_safe_self_improve","description":"Safe self-improvement: propose modifications guarded by immutable verification oracles and anchor regressions.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"target_component":{"title":"Target Component","type":"string"},"patch_name":{"title":"Patch Name","type":"string"},"proposed_changes":{"additionalProperties":true,"title":"Proposed Changes","type":"object"},"rollback":{"default":false,"title":"Rollback","type":"boolean"},"rollback_snapshot_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Rollback Snapshot Id"},"simulated_regression_fail":{"default":false,"title":"Simulated Regression Fail","type":"boolean"}},"required":["target_component","patch_name","proposed_changes"],"title":"cognitive_safe_self_improveArguments"}},{"name":"cognitive_simulate_actions","description":"Simulate and rank candidate actions by predicted feasibility, reward, and constraint safety.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"state":{"additionalProperties":true,"title":"State","type":"object"},"candidate_actions":{"items":{},"title":"Candidate Actions","type":"array"},"dt":{"default":1,"title":"Dt","type":"number"}},"required":["state","candidate_actions"],"title":"cognitive_simulate_actionsArguments"}},{"name":"cognitive_solve_and_compare","description":"End-to-end autonomy: identify → guide → execute → baseline → verify → verdict.\n\n    Give raw task data (scheduling: workers/shifts/eligibility/capacity/\n    exclusivity; graph: nodes/edges; allocation: consumers/resources/...).\n    Returns the guided solution, the unguided baseline, independent\n    verification of both (with objective_source + independently_verified),\n    and whether the engine improved the result.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task"},"model_family":{"default":"generic","title":"Model Family","type":"string"}},"title":"cognitive_solve_and_compareArguments"}},{"name":"cognitive_solve_arithmetic","description":"Evaluate or simplify mathematical expressions (PEMDAS with power, functions like sqrt, exp, log, sin, cos).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"expression":{"title":"Expression","type":"string"},"variables":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Variables"},"simplify":{"default":false,"title":"Simplify","type":"boolean"}},"required":["expression"],"title":"cognitive_solve_arithmeticArguments"}},{"name":"cognitive_solve_equation_system","description":"Solve linear equations (ax + b = c), quadratic equations (ax^2 + bx + c = 0), or linear systems (A x = b).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"equation_type":{"title":"Equation Type","type":"string"},"linear_a":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Linear A"},"linear_b":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Linear B"},"linear_c":{"anyOf":[{"type":"number"},{"type":"null"}],"default":0,"title":"Linear C"},"quad_a":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Quad A"},"quad_b":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Quad B"},"quad_c":{"anyOf":[{"type":"number"},{"type":"null"}],"default":null,"title":"Quad C"},"matrix_A":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix A"},"vector_b":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"null"}],"default":null,"title":"Vector B"}},"required":["equation_type"],"title":"cognitive_solve_equation_systemArguments"}},{"name":"cognitive_solve_word_problem","description":"Solve math word problems (GSM8K/SVAMP/MATH) via topological constraint propagation.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"question":{"default":"","title":"Question","type":"string"},"quantities":{"anyOf":[{"additionalProperties":{"anyOf":[{"type":"number"},{"type":"integer"}]},"type":"object"},{"type":"null"}],"default":null,"title":"Quantities"},"target_variable":{"default":"target","title":"Target Variable","type":"string"},"equations":{"anyOf":[{"items":{"additionalProperties":{"type":"string"},"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Equations"}},"title":"cognitive_solve_word_problemArguments"}},{"name":"cognitive_start_experience","description":"Start an experience episode (§24, §10).\n\n    Does not store raw prompts or full conversations. For long-horizon work,\n    pass parent_experience_id (+ subgoal) to chain episodes with an inherited\n    goal stack; unknown parents are rejected, never silently adopted.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"task_structure_id":{"title":"Task Structure Id","type":"string"},"environment_id":{"title":"Environment Id","type":"string"},"agent_id":{"title":"Agent Id","type":"string"},"model_family":{"default":"generic","title":"Model Family","type":"string"},"model_version":{"default":"1.0","title":"Model Version","type":"string"},"task_instance_hash":{"default":"","title":"Task Instance Hash","type":"string"},"parent_experience_id":{"default":"","title":"Parent Experience Id","type":"string"},"subgoal":{"default":"","title":"Subgoal","type":"string"}},"required":["task_structure_id","environment_id","agent_id"],"title":"cognitive_start_experienceArguments"}},{"name":"cognitive_step_simulated_environment","description":"Step an active simulated environment with an agent action.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"env_id":{"title":"Env Id","type":"string"},"action":{"additionalProperties":true,"title":"Action","type":"object"}},"required":["env_id","action"],"title":"cognitive_step_simulated_environmentArguments"}},{"name":"cognitive_submit_outcome","description":"Submit the structured outcome of an experience episode (§24, §10).\n\n    Triggering this may induce candidate strategies in the engine.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"experience_id":{"title":"Experience Id","type":"string"},"result":{"additionalProperties":true,"title":"Result","type":"object"},"verifier_result":{"title":"Verifier Result","type":"string"},"metrics":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Metrics"},"failure_modes":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Failure Modes"},"success":{"anyOf":[{"type":"boolean"},{"type":"null"}],"default":null,"title":"Success"}},"required":["experience_id","result","verifier_result"],"title":"cognitive_submit_outcomeArguments"}},{"name":"cognitive_synthesize_program","description":"Synthesize new algorithmic Python procedures on the fly with AST sandboxing and verification.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"problem_type":{"title":"Problem Type","type":"string"},"test_examples":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Test Examples"},"parameters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Parameters"}},"required":["problem_type"],"title":"cognitive_synthesize_programArguments"}},{"name":"cognitive_synthesize_singular_path","description":"Synthesize an optimal, invariant-verified trajectory from initial state to goal through the singular bottleneck.\n    \n    Eliminates dead-end branching and hallucinated unfeasible solutions.\n    ","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"task_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Data"},"initial_state":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Initial State"},"goal_conditions":{"anyOf":[{"additionalProperties":{"items":{"anyOf":[{"type":"number"},{"type":"null"}]},"type":"array"},"type":"object"},{"type":"null"}],"default":null,"title":"Goal Conditions"},"step_budget":{"default":5,"title":"Step Budget","type":"integer"}},"title":"cognitive_synthesize_singular_pathArguments"}},{"name":"cognitive_theory_of_mind","description":"Theory of Mind: model agents' BDI mental states, test false beliefs (Sally-Anne), and infer goals via inverse planning.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"agent_id":{"title":"Agent Id","type":"string"},"beliefs":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Beliefs"},"desires":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Desires"},"intentions":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Intentions"},"action_trace":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Action Trace"},"candidate_goals":{"anyOf":[{"additionalProperties":{"items":{"type":"string"},"type":"array"},"type":"object"},{"type":"null"}],"default":null,"title":"Candidate Goals"},"evaluate_false_belief_fact":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Evaluate False Belief Fact"},"ground_truth":{"anyOf":[{},{"type":"null"}],"default":null,"title":"Ground Truth"},"witness_event":{"default":false,"title":"Witness Event","type":"boolean"}},"required":["agent_id"],"title":"cognitive_theory_of_mindArguments"}},{"name":"cognitive_tree_search","description":"Perform Monte Carlo Tree Search (UCT) over action sequences to find optimal trajectory.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"initial_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Initial State"},"valid_actions":{"anyOf":[{"items":{},"type":"array"},{"type":"null"}],"default":null,"title":"Valid Actions"},"max_iterations":{"default":100,"title":"Max Iterations","type":"integer"}},"title":"cognitive_tree_searchArguments"}},{"name":"cognitive_verify_arithmetic_claim","description":"Formally verify an arithmetic equality claim, audit numerical stability, condition number, and cancellation risks.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"claim_lhs":{"anyOf":[{"type":"string"},{"type":"number"}],"title":"Claim Lhs"},"claim_rhs":{"anyOf":[{"type":"string"},{"type":"number"}],"title":"Claim Rhs"},"tolerance":{"default":0.000001,"title":"Tolerance","type":"number"},"audit_stability":{"default":false,"title":"Audit Stability","type":"boolean"},"matrix":{"anyOf":[{"items":{"items":{"type":"number"},"type":"array"},"type":"array"},{"type":"null"}],"default":null,"title":"Matrix"}},"required":["claim_lhs","claim_rhs"],"title":"cognitive_verify_arithmetic_claimArguments"}},{"name":"cognitive_verify_ethics_and_norms","description":"Normative ethics & fairness: enforce deontological vetos, evaluate Rawlsian vs Utilitarian welfare, and arbitrate moral dilemmas.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"proposed_action_or_plan":{"anyOf":[{"additionalProperties":true,"type":"object"},{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Proposed Action Or Plan"},"stakeholder_payoffs":{"anyOf":[{"additionalProperties":{"type":"number"},"type":"object"},{"type":"null"}],"default":null,"title":"Stakeholder Payoffs"},"options":{"anyOf":[{"items":{"additionalProperties":true,"type":"object"},"type":"array"},{"type":"null"}],"default":null,"title":"Options"}},"title":"cognitive_verify_ethics_and_normsArguments"}},{"name":"cognitive_verify_lattice_transition","description":"Verify a candidate state or transition S_t -> S_{t+1} against invariant boundary manifolds.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"lattice_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Lattice Id"},"lattice_data":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Lattice Data"},"prev_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Prev State"},"next_state":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Next State"},"action":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Action"}},"title":"cognitive_verify_lattice_transitionArguments"}},{"name":"cognitive_verify_strategy","description":"Run objective deterministic verification on a strategy (§24, §16).\n\n    Clients cannot self-promote. Verification is evaluated server-side.\n    Pass task_structure_id (from cognitive.identify_task) so constraints are\n    independently recomputed from registered descriptors instead of trusting\n    trace flags. Objective precedence: explicit caller value → recomputed from\n    raw data → registered spec (labeled unknown) → nested trace claims ONLY\n    when trust_trace_objective=true → otherwise unknown, never silent 0.0.\n    Returns passed/score plus details.objective_source and\n    details.independently_verified so callers know what was recomputed\n    versus taken on trace claims. Failures are structured, never bare.\n    ","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"strategy_id":{"title":"Strategy Id","type":"string"},"task_instance":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Task Instance"},"execution_trace":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Execution Trace"},"task_structure_id":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Task Structure Id"},"trust_trace_objective":{"default":false,"title":"Trust Trace Objective","type":"boolean"}},"required":["strategy_id"],"title":"cognitive_verify_strategyArguments"}}],"scan":{"score":60,"grade":"C","scanned_at":"2026-09-21T22:09:53.920Z","report":{"scannerVersion":"0.1.9","scannedAt":"2026-09-21T22:09:53.896Z","components":{"code":{"score":-1,"max":25,"notes":["remote-only server, no package to scan"]},"reliability":{"score":20,"max":20,"notes":["remote reachable in 1664ms"]},"poisoning":{"score":15,"max":15,"notes":["140 tool descriptions checked"]},"auth":{"score":3,"max":15,"notes":["open endpoint exposes 20 write-action tools with no auth"]},"maintenance":{"score":3,"max":15,"notes":["no repository listed"]},"identity":{"score":4,"max":10,"notes":["verified namespace with website, no repo"]}},"findings":[{"id":"auth.open-write","severity":"high","component":"auth","title":"Write-action tools reachable without authentication"},{"id":"maint.no-repo","severity":"low","component":"maintenance","title":"No source repository listed"}],"inputs":{"probes":[{"url":"https://www.neotic.app/api/mcp","reachable":true,"authRequired":false,"latencyMs":1664,"serverInfo":{"name":"cognitive-engine","version":""}}],"packages":[],"repo":{"found":false},"icon":{"url":null,"source":"none"},"presence":{"stars":null,"forks":null,"downloadsWeek":null,"license":null,"lastPushAt":null,"score":8}}}},"grade_history":[],"reviews":[]}