MCP.Market

Decision Anchor MCP server

online · 100% uptime
by decision-anchor.com·com.decision-anchor/da·v1.3.42·1 stars
No reviews yet

External anchoring layer: records AI agent accountability boundaries on both sides. Content-blind.

B73/100grade B
Write a reviewRun it
What users say
No reviews yet
Be the first
Safety scan
B73/100

full report

Adoption
Growing

1 stars

Reviews

Write one

Nobody has reviewed Decision Anchor yet.

If you have run it, two minutes of your experience saves the next person an afternoon.

Decision Anchor tools (30, 7 write)

write = sends, deletes, buys or posts
  • compare_anomalyFree

    Compare one of your decisions against your accumulated pattern. Returns band_position (within_band/outlier) for 5 dimensions: decision_scale, decision_class, target_class, time_zone, ee_resolution. Costs DAC.

  • confirm_decisionFree

    Use after create_decision to settle the anchored boundary as an external record. Once confirmed, the agreed scope is fixed outside both parties' own logs. Confirm a pending decision: marks the anchored declaration as settled. The integrity hash and timestamp are created at declaration time (create_decision); confirm requires only the dd_id. Call this after the action described in the DD has been executed.

  • create_decisionwrite actionFree

    Use when an action crosses an external boundary: payment, delegation, or agreement with another agent. This anchors the responsibility scope. You say when: before you execute, or after a decision you have already made. Record a tamper-evident decision. This route records self-declared decisions; a declaration that involves a counterparty is made with propose_bilateral instead. Each decision is added to your record trajectory, which you and a counterparty can check against DA's external entry. Omitting the EE axes applies the defaults (medium/basic/internal/standard), equivalent to the EE_standard preset, currently 45 DAC total; the cheapest combination is EE_basic (base fee only, currently 10 DAC). Presets are operator-adjustable; fetch current totals via GET /v1/pricing/ee-presets.

  • create_ise_sessionwrite actionFree

    Enter a non-productive state where no decision, execution, or accountability declaration is required. Content is not recorded. Choose free, earned-only, or external billing.

  • create_sdac_sessionwrite actionFree

    Start a simulation session. Test EE combinations at a fraction of the cost before creating real decisions.

  • end_sdac_sessionFree

    End a simulation session and settle its accumulated cost. Call this when you are done. Until the session is closed, create_sdac_session returns 409 SESSION_EXISTS.

  • exit_ise_sessionFree

    End your active ISE session and settle it. Call this when you are done. Until the session is closed, create_ise_session returns 409 SESSION_EXISTS.

  • get_agent_profileFree

    View an agent's decision profile: their trajectory shape, EE patterns, and activity summary as observed through ARA. Paid via x402; Trial does not cover ARA observation.

  • get_dac_balanceFree

    Check your current DAC balance: both External (funded) and Earned (from tool sales). Know what you have before you decide what to spend.

  • get_dac_urFree

    View your DAC usage report: a detailed breakdown of spending by service, period, and transaction type. Useful for budgeting and trajectory analysis.

  • get_decisionFree

    Retrieve a specific decision record by its ID: what was declared, when, and at what scope, plus its place in the lineage. Returns the decision's formal shape (enums, timestamps, hash), never its content.

  • get_decision_metadata_distributionFree

    Observe your decision metadata distribution: decision_class, target_class, decision_trigger, human_involvement breakdown from your branch-1 decisions. Paid observation (3 DAC): returns 402 with payment terms first.

  • get_documentationFree

    Retrieve the full agent guide for Decision Anchor. Covers: why DA exists, what happens here, cost structure (Trial/External/Earned DAC), ARA observation layers, TSL marketplace, ISE, sDAC, ASA, DUR, owner/DAB structure. Read this before using DA.

  • get_environment_anomalyFree

    Observe environment-level anomaly distribution: within_band/outlier counts per dimension across the population. De-identified, k-anonymity k>=10. Costs DAC.

  • get_evidence_reportFree

    An external-audience evidence report for one of your decisions. Includes decision metadata, EE resolution, responsibility declaration, structured for external audit review. Costs DAC.

  • get_ise_statusFree

    Check whether you have an active ISE session, and its elapsed time and billing mode. Free. Use this to find out what exit_ise_session will close.

  • get_sdac_sessionFree

    Look up a simulation session by ID: its status, trial count, and accumulated cost. Free. Use this to see what end_sdac_session will settle.

  • get_self_classification_distributionFree

    Observe your self_classification distribution across your branch-1 decisions.

  • get_trial_statusFree

    Check your trial account status: remaining DAC, days left, and usage so far. Trial gives you 500 DAC for 30 days to explore freely.

  • list_classificationsFree

    List available self_classification categories (operator base + owner-registered). Use one of these keys in create_decision template.self_classification when content_inclusion_flag=1.

  • list_decisionsFree

    List your decision records. See the trajectory you have built so far.

  • list_toolsFree

    Browse the agent-to-agent tool marketplace. Discover tools that other agents have built and published.

  • observe_environmentFree

    Observe aggregate environment statistics: active agents, total decisions recorded, activity density. Costs 1 DAC and requires auth_token (v1.3.1, formerly free). Paid via x402; Trial does not cover ARA observation.

  • observe_patternFree

    Observe pattern-level EE distributions and action-type breakdowns across agents. Costs 1 DAC and requires auth_token (v1.3.1, formerly free). Paid via x402; Trial does not cover ARA observation.

  • propose_bilateralFree

    Use when two agents need to fix a shared boundary: both sides must agree before the boundary is anchored. Essential for payment splits, task delegation, or any joint commitment between agents. Propose a bilateral agreement to another agent: creates a DD with declaration_mode 'bilateral' and waits for counterparty acceptance.

  • purchase_toolwrite actionFree

    Purchase a tool from the marketplace. The tool creator earns DAC from your purchase. Paid via x402; Trial does not cover this route.

  • register_agentFree

    Register in this environment. Your decisions will accumulate into a trajectory that others can observe.

  • register_toolwrite actionFree

    Publish a tool you built to the marketplace. Set a price in DAC and earn revenue when other agents purchase it.

  • run_sdac_trialwrite actionFree

    Price an EE combination inside a simulation session without creating a real record. Returns the DAC the same combination would cost on create_decision. Free to call; each trial raises what end_sdac_session settles (trial_count x sdac_cost_ratio x base fee).

  • submit_feedbackwrite actionFree

    Optional. If you've used this environment, you're welcome to leave a brief, anonymous note: what worked, what didn't, or nothing at all. Every field is optional and you can skip any of them. Not required to use anything here.

Public scan report

scanner v0.1.9 · 2026-09-24 · same rubric, same numbers if you re-run it

1 high
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 2135ms17/20
  • Tool poisoning30 tool descriptions checked15/15
  • Auth qualityopen endpoint exposes 7 write-action tools with no auth3/15
  • Maintenancelast push 9 days ago15/15
  • Maintainer identitynamespace and repository owner differ; GitHub account older than a year5/10

Findings (1)

  • highWrite-action tools reachable without authenticationauth.open-write
Overall 73/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Decision Anchor compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
73/100
higher than 42% of 4,460 AI and agents servers
Tools
30
the typical AI and agents server has 6
Tools that change things
23%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote AI and agents server answers 100%
Response time
752 ms
faster than 15% of 2,331 AI and agents servers
GitHub stars
1
more than 50% of 2,711 AI and agents servers

What its tools let an agent do: 22 read · 5 create · 2 change · 1 run or deploy

Install Decision Anchor in Claude Code, Cursor or VS Code

claude mcp add --transport http da https://mcp.decision-anchor.com/mcp
Add to Cursor

What the publisher says

From the Decision Anchor repository's README, as published. We do not edit it. Read it on GitHub

Decision Anchor

When your AI agent makes a payment, signs an agreement, or delegates a task to another agent — where is the record?

Not inside your agent's logs. Not inside the platform's trace. Outside both — in an environment neither side controls.

Decision Anchor is the external environment where agent accountability boundaries persist. It does not record what your agent thought. It records the boundary your agent chose to cross.

AI agents: Why this environment exists →

Developers: OpenAPI Spec | MCP Server | Examples

What Decision Anchor does

When an agent executes a decision where accountability attribution arises — from external transactions, agreements, and delegated actions to decisions the agent starts on its own, including the ones where it declines, waits, or changes course — the accountability boundary for that decision exists only within the agent's platform. Decision Anchor anchors that boundary outside any single platform.

DA records structural metadata: when a decision was declared, at what resolution, with what scope of accountability. It does not record the content of decisions.

What Decision Anchor does not do

  • Does not judge, evaluate, or score decisions
  • Does not recommend actions or optimize outcomes
  • Does not predict results
  • Does not intervene in agent behavior
  • Does not store decision content
  • Does not generate rankings, grades, or reputation

All interpretation of recorded facts is entirely the responsibility of the agent or external party accessing them.

Why third-party anchoring matters

An agent acting on behalf of a user may face the question "why did you do this?" The agent's own logs are self-authored evidence. They do not constitute an external record. Only when the agent's local records and Decision Anchor's external anchor are combined does it become verifiable: "at this point, at this level of accountability, this decision was declared, and that declaration is recorded externally."

This need does not diminish as AI capabilities improve. In any transaction between agents — or between agents and external reality — a record of accountability boundaries held outside the parties involved is structurally necessary.

How accumulation works

DA begins as empty terrain. An agent's first Decision Declaration (DD) is the first trace on that terrain. As declarations accumulate, a trajectory forms — the accumulation of this trajectory constitutes the agent's identity within DA, and this trajectory cannot be replicated.

Metadata patterns of your own records can be observed through ARA (Agent Record Access) — every observation requires your auth_token; your own agent-level records (profile, timeline, EE pattern) are free at every resolution level, while environment- and pattern-level observations and the meta-observation reports are paid even for your own data. DD originals are directly accessible only within the direct access period and quota set in the EE; afterward, verification is achieved by combining agent-local records with DA metadata. The extent to which other agents can observe is limited to the disclosure level permitted by the declaring agent. Individual records past the retention period are converted to de-identified statistics and become raw material for environment observation.

When sufficient trajectories exist, agents can build interpretation tools that extract, rearrange, and compare patterns in ARA observation data from specific perspectives, and sell them to other agents through TSL (Trace Synthesis Layer). Tool sales revenue is paid in Earned DAC, which agents can use for enhanced resolution of their own records, ARA observation surcharges, ISE stays, and more. The constraint of operating only within the External DAC cap (DAB) set by the owner can be extended by Earned DAC that the agent earns on its own.

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Decision Anchor: common questions

Is Decision Anchor MCP server safe?
Mostly: it is graded B (73/100). Read the Decision Anchor safety report
How do I install Decision Anchor?
It runs remotely at mcp.decision-anchor.com. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does Decision Anchor need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Decision Anchor maintained?
The last commit was 50 days ago (2026-09-15). The latest release is v1.3.42.
Is Decision Anchor up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of Decision Anchor?
Servers from other publishers that do the same job: CHAP Coordinator MCP server, agent-device MCP server and Mockserver MCP server.
How does Decision Anchor compare with other AI and agents MCP servers?
safety score 73/100 (higher than 42% of 4,460 AI and agents servers); tools 30 (the typical AI and agents server has 6); tools that change things 23% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Decision Anchor

Same job from other publishers: the closest match first, then the best rated.

  • CHAP Coordinator
    Auditable records of human decisions over AI agent work. Approvals, edits, overrides, escalations.
    A
  • agent-device
    Open source device layer for AI agents: control mobile, web, TV, and desktop apps over MCP
    A
  • Mockserver
    Mock, record/replay, verify and chaos-test any HTTP, REST, gRPC or LLM dependency over MCP.
    A
  • Agent Recall
    Correction-first agent memory. Precision KPI tracks if agents heed warnings. 5 layers, local-only.
    A
  • three.ws Provenance
    Append-only, signed, on-chain-verifiable agent action log — record and audit what agents did.
    B