OraClaw

MCP Optimization Tools for AI Agents -- 17 tools, 21 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.

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Validation

OraClaw's math has been independently implemented in 12 open-source projects across AI agent orchestration, time-series tracking, vector search, MIP optimization, and production ML systems -- all within the first 8 days after public launch.

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

• chernistry/bernstein -- 84⭐ agent orchestration framework. LinUCB contextual router with α=0.3, shadow-evaluation path, interpretable decision reasons. Shipped in codex/issue-367-linucb-router 1h40m after the spec correction.
• stxkxs/nanohype -- contextual bandit routing, pluggable strategy registry (hash / sliding-TTL / semantic), cost anomaly detection, LinUCB on roadmap. "Your input shaped a lot of what actually shipped."
• rfivesix/hypertrack -- Bayesian/Kalman-style adaptive calorie estimator with phase-aware kcal/kg ramp. Shipped in 0.8.0-beta. "At this point I think the mathematical model is in a very strong place."
• AlanHuang99/pyrollmatch -- entropy balancing (Hainmueller 2012) with moment constraints + max_weight cap. Shipped in v0.1.3.
• stffns/vstash -- IDF-sigmoid relevance weighting. Shipped in v0.17.0.

Marketplace distribution:

• ✓ punkpeye/awesome-mcp-servers (84K⭐) -- merged
• ✓ TensorBlock/awesome-mcp-servers -- merged
• ✓ MCP Registry, Glama (AAA rating), PulseMCP, Smithery, toolsdk-ai -- listed

Maintainer relationships (warm technical correspondence): Qdrant, Milvus, NetworkX, Apache DataFusion, DuckDB, pymc-labs.

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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

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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.

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MCP Tool Catalog (17 tools)

Free tier (11 tools, no API key — 25 calls/day per IP):

| Tool | What It Does | Latency | |------|-------------|---------| | optimize_bandit | UCB1 / Thompson / Epsilon-Greedy arm selection | 0.01ms | | optimize_contextual | Context-aware LinUCB bandit | 0.05ms | | optimize_evolve | Genetic algorithm for discrete + multi-objective problems | <10ms | | solve_schedule | Energy-matched task scheduling | 3ms | | score_convergence | Multi-source probability consensus (Hellinger) | 0.04ms | | score_calibration | Brier + log score for forecaster accuracy | 0.02ms | | predict_bayesian | Beta posterior update from weighted evidence | 0.05ms | | predict_ensemble | Multi-model consensus + uncertainty decomposition | 0.1ms | | plan_pathfind | A* + Yen's k-shortest paths | 0.1ms | | simulate_montecarlo | Single-factor Monte Carlo (6 distributions) | <2ms | | simulate_scenario | What-if comparison + sensitivity ranking | <5ms |

Premium tier (6 tools, requires ORACLAW_API_KEY):

| Tool | What It Does | Latency | |------|-------------|---------| | optimize_cmaes | CMA-ES continuous black-box optimization | 12ms | | solve_constraints | LP / MIP / QP solver via HiGHS (provably optimal) | 2ms | | analyze_graph | PageRank, Louvain communities, bottleneck detection | 0.5ms | | analyze_risk | VaR and CVaR (Expected Shortfall) | <2ms | | predict_forecast | ARIMA + Holt-Winters time series forecasting | 0.08ms | | detect_anomaly | Z-Score + IQR anomaly detection | 0.01ms |

14 of 18 REST endpoints respond in under 1ms. All under 25ms.

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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}'

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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.

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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/ |

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Building with OraClaw?

We'd love to hear what you're working on. Share your use case, ask questions, or request features:

• Tell us what you're building
• Report an issue
• Join the conversation on Moltbook

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Links

• Live API: https://oraclaw-api.onrender.com
• Dashboard: https://web-olive-one-89.vercel.app
• npm: https://www.npmjs.com/org/oraclaw
• Demo: https://web-olive-one-89.vercel.app/demo
• GitHub: https://github.com/Whatsonyourmind/oracle

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If this saved your agent from hallucinating math, star us :star:

License

MIT