OraClaw MCP Server
17 decision-intelligence tools your AI agent can call. Sub-25ms. 11 free without a key.
{
"mcpServers": {
"oraclaw": {
"command": "npx",
"args": ["-y", "@oraclaw/mcp-server"]
}
}
}
Drop that into your Claude Desktop / Cursor / Cline MCP config and your agent can immediately:
"I have 3 email subject line variants with these click rates. Which should I send next?"
The agent calls optimize_bandit → gets a statistically optimal arm in 0.01ms:
{ "selected": { "id": "C", "name": "Option C" },
"score": 1.876, "exploitation": 0.9, "exploration": 0.976, "regret": 0.1 }
No LLM math, no hallucinated formulas, no setup beyond the snippet above.
| Free (no key) | What it does |
|---|
optimize_bandit | UCB1 / Thompson / ε-greedy arm selection |
optimize_contextual | LinUCB contextual bandit (per-situation choice) |
optimize_evolve | Genetic algorithm (discrete + multi-objective) |
solve_schedule | Task → time-slot assignment with energy matching |
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 |
| Premium (need API key) | What it does |
|---|
optimize_cmaes | CMA-ES continuous black-box optimization |
solve_constraints | LP / MIP / QP solver (HiGHS, provably optimal) |
analyze_graph | PageRank + Louvain + critical path + bottlenecks |
analyze_risk | Portfolio VaR / CVaR with correlation |
predict_forecast | ARIMA / Holt-Winters time series |
detect_anomaly | Z-score / IQR outlier detection |
Every tool ships with an explicit inputSchema, outputSchema, and MCP behavior annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) so your agent knows exactly what it gets back.
oraclaw signup → — one email field, instant key, no card needed. 1,000 calls/day on pay-per-call ($0.005/call). Upgrade to Starter $9/mo for 50k/month.
Then add ORACLAW_API_KEY to your MCP env:
{
"mcpServers": {
"oraclaw": {
"command": "npx",
"args": ["-y", "@oraclaw/mcp-server"],
"env": { "ORACLAW_API_KEY": "your-key-here" }
}
}
}
Use the API directly (no MCP)
curl -X POST https://oraclaw-api.onrender.com/api/v1/optimize/bandit \
-H "Content-Type: application/json" \
-d '{
"arms": [
{"id":"A", "name":"Option A", "pulls":10, "totalReward":7},
{"id":"B", "name":"Option B", "pulls":10, "totalReward":5}
],
"algorithm": "ucb1"
}'
Free tier: 25 calls/day per IP, no key required.
Why agents need this
LLMs can't reliably do: bandit selection, LP optimization, time-series forecasting, graph centrality, Monte Carlo sampling, or anomaly detection. They confabulate the math. OraClaw is the deterministic substrate underneath your agent — every answer is a real algorithm, returns structured JSON, and runs in under 25ms.