MCP.Market

Oraclaw MCP server

by Whatsonyourmind·io.github.Whatsonyourmind/oraclaw·v1.5.1·13 stars
No reviews yet

Decision intelligence MCP — 17 tools (optimize, simulate, predict, score, graph). Sub-25ms.

B80/100grade B
Write a reviewInstall
What users say
No reviews yet
Be the first
Safety scan
B80/100

full report

Adoption
Growing

13 stars47 downloads/wk

Reviews

Write one

Nobody has reviewed Oraclaw yet.

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

Oraclaw tools (17, 2 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • analyze_graph

    [Premium] Compute structural metrics of a directed weighted graph: PageRank centrality, Louvain community clusters,

  • analyze_risk

    [Premium] Compute portfolio Value-at-Risk and Conditional VaR (Expected Shortfall) from a historical [asset][time]

  • detect_anomaly

    [Premium] Flag outlier points in a numeric series using a Z-score test (parametric, assumes near-normal) or IQR test

  • optimize_bandit

    Select the next option to try from 2+ variants that each have observed pull/reward history, balancing exploitation

  • optimize_cmaes

    [Premium] Optimize N continuous parameters against a weighted-sum objective using CMA-ES, suited to

  • optimize_contextual

    Select the best option given a numeric context/feature vector, using a LinUCB contextual bandit that learns

  • optimize_evolvewrite action

    Run a genetic algorithm over a fixed-length gene vector (binary, integer, real, or permutation bounds) against a

  • plan_pathfind

    Find the shortest path (or k-shortest paths) between a start and end node in a weighted directed graph using A* with

  • predict_bayesianwrite action

    Update a prior probability with weighted evidence signals using a Beta posterior (the prior seeds Beta(prior*10,

  • predict_ensemble

    Combine 2+ model point predictions into one consensus using weighted voting, stacking, or Bayesian model averaging,

  • predict_forecast

    [Premium] Forecast the next N values of one evenly-spaced numeric time series using ARIMA (non-seasonal trend) or

  • score_calibration

    Measure how well a set of probability predictions matched observed binary outcomes, returning the Brier score and log

  • score_convergence

    Score how strongly multiple independent sources agree on a single event's probability, using Hellinger-distance

  • simulate_montecarlo

    Draw N samples from one parametric distribution (normal, lognormal, uniform, triangular, beta, or exponential) and

  • simulate_scenario

    Compare named what-if scenarios against a base case where the outcome metric is the sum of the input variables, and

  • solve_constraints

    [Premium] Solve a linear / mixed-integer / quadratic program with the HiGHS solver and return a provably optimal

  • solve_schedule

    Assign tasks to time slots to maximize total score by matching each task's energy requirement to a slot's energy

Public scan report

scanner v0.1.9 · 2026-09-26 · same rubric, same numbers if you re-run it

no findings
  • Code scan3 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 2 days ago15/15
  • Maintainer identityregistry namespace matches repository owner6/10
Overall 80/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Oraclaw compares

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

Safety score
80/100
higher than 60% of 48,255 servers
Tools
17
the typical server has 7
Tools that change things
12%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
13
more than 89% of 28,688 servers
Downloads a week
47
more than 39% of 12,191 servers with a package

What its tools let an agent do: 15 read · 2 change

Install Oraclaw in Claude Code, Cursor or VS Code

Runs npx -y @oraclaw/mcp-server on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add oraclaw -- npx -y @oraclaw/mcp-server
Add to Cursor

What the publisher says

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

OraClaw

MCP Optimization Tools for AI Agents -- 17 tools (11 free, no key), sub-25ms. Zero LLM cost.

Your AI agent can't do math. OraClaw gives it deterministic optimization, simulation, forecasting, and risk analysis through the Model Context Protocol. Every tool returns structured JSON, runs in under 25ms, and costs nothing to compute.

🚀 Using OraClaw in production — or want managed hosting, premium tools, or priority support?

Tell me about your use case → — I read every one.

💬 Building something with it? Star the repo and say hi in Discussions — what you build steers what I ship next.

What this solves

LLMs generate plausible text, not mathematically optimal answers. OraClaw gives an AI agent a set of deterministic numerical tools it can call instead of guessing — each returns structured JSON from a real algorithm, with no token spend on reasoning. Concretely:

  • Your agent needs to pick the next variant to try (A/B test arm, ad/email copy, recommendation) and balance exploration against exploitation — without hand-rolling a bandit or letting the model eyeball it. Call optimizebandit (or optimizecontextual when the best choice depends on per-call features).
  • Your agent needs a provably optimal allocation or schedule under hard constraints (budget split, integer counts, capacity caps) — without the model hallucinating constraints. Call solveconstraints (LP/MIP/QP via HiGHS) or solveschedule for task-to-slot fitting.
  • Your agent needs to quantify uncertainty around an outcome — project a value under an uncertain input, or measure VaR/CVaR on a weighted multi-asset book with auditable assumptions — without a Monte Carlo loop in the prompt. Call simulatemontecarlo, simulatescenario, or analyze_risk.
  • Your agent needs a point forecast or an outlier flag on a time series (demand, KPIs, sensor/metric streams) — without inventing trend math. Call predictforecast (ARIMA / Holt-Winters) or detectanomaly (Z-score / IQR).
  • Your agent needs to fuse or score probability signals — combine model outputs, measure how much independent sources agree, or check whether past predictions were well-calibrated. Call predictensemble, scoreconvergence, or score_calibration.
  • Your agent needs to reason over a graph — rank influential nodes, cluster a dependency/knowledge graph, find a critical path, or route between two nodes. Call analyzegraph or planpathfind.

Where the algorithms have been used

OraClaw's algorithms have informed implementations in several open-source projects -- through contributed routing specs, algorithm guidance, and shared math -- spanning AI agent orchestration, time-series tracking, vector search, and optimization.

Selected contributions (see CHANGELOG.md for the full list):

Shortened. The full README is on GitHub.

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

Oraclaw: common questions

Is Oraclaw MCP server safe?
Mostly: it is graded B (80/100). Read the Oraclaw safety report
How do I install Oraclaw?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Oraclaw need an API key?
Yes. The registry entry asks for ORACLAW_API_KEY.
Is Oraclaw maintained?
The last commit was 40 days ago (2026-09-25). The latest release is v1.5.1.
What can I use instead of Oraclaw?
Servers from other publishers that do the same job: Codebase Memory MCP server and State Memory MCP server.
How does Oraclaw compare with other MCP servers?
safety score 80/100 (higher than 60% of 48,255 servers); tools 17 (the typical server has 7); tools that change things 12% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Oraclaw

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

  • Codebase Memory
    Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
    C
  • State Memory MCP
    Persistent workflow graph server tracking tasks, decisions, spatial entities, and blockers.
    A