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…
oraclaw is an MCP project maintained in the whatsonyourmind/oraclaw repository. The maintainer describes its purpose as “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…”. Read the project documentation for the complete scope.
The documented npm package is @oraclaw/mcp-server. The configuration below comes from the maintainer’s example; adapt its paths and account values to your environment.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| optimize_bandit | UCB1 / Thompson / Epsilon-Greedy arm selection |
| optimize_contextual | Context-aware LinUCB bandit |
| optimize_evolve | Genetic algorithm for discrete + multi-objective problems |
| solve_schedule | Energy-matched task scheduling |
| score_convergence | Multi-source probability consensus (Hellinger) |
| score_calibration | Brier + log score for forecaster accuracy |
| predict_bayesian | Beta posterior update from weighted evidence |
| predict_ensemble | Multi-model consensus + uncertainty decomposition |
| plan_pathfind | A + Yen's k-shortest paths |
| simulate_montecarlo | Single-factor Monte Carlo (6 distributions) |
| simulate_scenario | What-if comparison + sensitivity ranking |
| optimize_cmaes | CMA-ES continuous black-box optimization |
| solve_constraints | LP / MIP / QP solver via HiGHS (provably optimal) |
| analyze_graph | PageRank, Louvain communities, bottleneck detection |
| analyze_risk | VaR and CVaR (Expected Shortfall) |
| predict_forecast | ARIMA + Holt-Winters time series forecasting |
| detect_anomaly | Z-Score + IQR anomaly detection |
{
"mcpServers": {
"oraclaw": {
"command": "npx",
"args": [
"-y",
"@oraclaw/mcp-server"
]
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
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