MCP 服务器

oraclaw-mcp-server

17 decision intelligence algorithms as MCP tools for AI agents. Bandits (UCB1, Thompson), LP/MIP solver (HiGHS), Monte Carlo simulation, Bayesian inference, graph analytics (PageRank, Louvain), genetic algorithms, CMA-ES, anomaly detection, time series forecasting, and more. All under 25ms, deterministic, zero LLM cost.

README

OraClaw

MCP Optimization Tools for AI Agents -- 12 tools, 19 algorithms, 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.

Quick Start

1. MCP Server (recommended for AI agents)

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "oraclaw": {
      "command": "npx",
      "args": ["-y", "@oraclaw/mcp-server"]
    }
  }
}

Then ask your agent:

"I have 3 email subject line variants. Which should I send next?"

The agent calls optimize_bandit and gets a statistically optimal selection in 0.01ms.

2. REST API (no install)

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},
      {"id": "C", "name": "Option C", "pulls": 2, "totalReward": 1.8}
    ],
    "algorithm": "ucb1"
  }'

Response (<1ms):

{
  "selected": { "id": "C", "name": "Option C" },
  "score": 1.876,
  "algorithm": "ucb1",
  "exploitation": 0.9,
  "exploration": 0.976,
  "regret": 0.1
}

Free tier: 25 calls/day, no API key needed.

3. npm SDK

npm install @oraclaw/bandit

import { OraBandit } from '@oraclaw/bandit';

const client = new OraBandit({ baseUrl: 'https://oraclaw-api.onrender.com' });
const result = await client.optimize({
  arms: [
    { id: 'A', name: 'Short Subject', pulls: 500, totalReward: 175 },
    { id: 'B', name: 'Long Subject', pulls: 300, totalReward: 126 },
  ],
  algorithm: 'ucb1',
});

14 SDK packages: @oraclaw/bandit, @oraclaw/solver, @oraclaw/simulate, @oraclaw/risk, @oraclaw/forecast, @oraclaw/anomaly, @oraclaw/graph, @oraclaw/bayesian, @oraclaw/ensemble, @oraclaw/calibrate, @oraclaw/evolve, @oraclaw/pathfind, @oraclaw/cmaes, @oraclaw/decide

Why?

LLMs generate plausible text, not optimal solutions. Ask GPT to pick the best A/B test variant and it applies a heuristic that ignores the exploration-exploitation tradeoff. Ask it to solve a linear program and it hallucinates constraints. OraClaw gives your agent access to real algorithms -- bandits, solvers, forecasters, risk models -- that return mathematically correct answers in sub-millisecond time, without burning tokens on reasoning.

MCP Tool Catalog (12 tools)

Tool	What It Does	Latency
`optimize_bandit`	A/B test selection via UCB1, Thompson Sampling, Epsilon-Greedy	0.01ms
`optimize_contextual`	Context-aware personalized selection via LinUCB	0.05ms
`optimize_cmaes`	Black-box continuous optimization (CMA-ES)	12ms
`solve_constraints`	LP/MIP/QP optimization via HiGHS solver	2ms
`solve_schedule`	Energy-matched task scheduling	3ms
`analyze_decision_graph`	PageRank, Louvain communities, bottleneck detection	0.5ms
`analyze_portfolio_risk`	VaR and CVaR (Expected Shortfall)	<2ms
`score_convergence`	Multi-source agreement scoring	0.04ms
`score_calibration`	Brier score and log score for prediction quality	0.02ms
`predict_forecast`	ARIMA and Holt-Winters time series forecasting	0.08ms
`detect_anomaly`	Z-Score and IQR anomaly detection	0.01ms
`plan_pathfind`	A* pathfinding with k-shortest paths	0.1ms

14 of 17 REST endpoints respond in under 1ms. All under 25ms.

Try It Now

The API is live. No signup required.

# Bayesian inference
curl -X POST https://oraclaw-api.onrender.com/api/v1/predict/bayesian \
  -H 'Content-Type: application/json' \
  -d '{"prior": 0.3, "evidence": [{"factor": "positive_test", "weight": 0.9, "value": 0.05}]}'

# Monte Carlo simulation
curl -X POST https://oraclaw-api.onrender.com/api/v1/simulate/montecarlo \
  -H 'Content-Type: application/json' \
  -d '{"simulations": 1000, "distribution": "normal", "params": {"mean": 100, "stddev": 15}}'

# Anomaly detection
curl -X POST https://oraclaw-api.onrender.com/api/v1/detect/anomaly \
  -H 'Content-Type: application/json' \
  -d '{"data": [10, 12, 11, 13, 50, 12, 11, 10], "method": "zscore", "threshold": 2.0}'

Pricing

Tier	Calls	Price	Auth
Free	25/day	$0	None
Pay-per-call	1K/day	$0.005/call	API key
Starter	10K/mo	$9/mo	API key
Growth	100K/mo	$49/mo	API key
Scale	1M/mo	$199/mo	API key

x402 USDC: AI agents pay $0.01-$0.15 per call with USDC on Base. No subscription, no API key.

Source Code

Component	Path
MCP Server	`mission-control/packages/mcp-server/`
REST API	`mission-control/apps/api/`
Algorithms	`mission-control/apps/api/src/services/oracle/algorithms/`
SDK Packages	`mission-control/packages/sdk/`
LangChain Tools	`mission-control/integrations/langchain/oraclaw_tools.py`
Mobile App	`mission-control/apps/mobile/`
Dashboard (Next.js)	`web/`