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 optimize_bandit (or optimize_contextual 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 solve_constraints (LP/MIP/QP via HiGHS) or solve_schedule 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 simulate_montecarlo, simulate_scenario, 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 predict_forecast (ARIMA / Holt-Winters) or detect_anomaly (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 predict_ensemble, score_convergence, 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 analyze_graph or plan_pathfind.

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):

• chernistry/bernstein -- agent orchestration framework. LinUCB contextual router (α=0.3) with shadow-evaluation path and interpretable decision reasons, shipped in codex/issue-367-linucb-router after a contributed spec correction.
• stxkxs/nanohype -- contextual bandit routing, pluggable strategy registry (hash / slid

... View full README on GitHub