MCP Servers

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W
Warpgbm MCP Service
by @jefferythewind
GitHub Repo(1 stars)

Train gradient boosting models quickly and export portable artifacts for smooth deployment. Run fast predictions from newly trained models or existing artifacts. Upload datasets, browse available backends, and access a practical guide with best practices and troubleshooting.

Created 10/17/2025
Updated about 2 months ago
README
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Repository documentation and setup instructions

⚡ WarpGBM MCP Service

GPU-accelerated gradient boosting as a cloud MCP service
Train on A10G GPUs • Get artifact_id for <100ms cached predictions • Download portable artifacts

🎯 What is This?

Outsource your GBDT workload to the world's fastest GPU implementation.

WarpGBM MCP is a stateless cloud service that gives AI agents instant access to GPU-accelerated gradient boosting. Built on WarpGBM (91+ ⭐), this service handles training on NVIDIA A10G GPUs while you receive portable model artifacts and benefit from smart 5-minute caching.

🏗️ How It Works (The Smart Cache Workflow)

graph LR
    A[Train on GPU] --> B[Get artifact_id + model]
    B --> C[5min Cache]
    C --> D[<100ms Predictions]
    B --> E[Download Artifact]
    E --> F[Use Anywhere]
  1. Train: POST your data → Train on A10G GPU → Get artifact_id + portable artifact
  2. Fast Path: Use artifact_id → Sub-100ms cached predictions (5min TTL)
  3. Slow Path: Use model_artifact_joblib → Download and use anywhere

Architecture: 🔒 Stateless • 🚀 No model storage • 💾 You own your artifacts

⚡ Quick Start

For AI Agents (MCP)

Add to your MCP settings (e.g., .cursor/mcp.json):

{
  "mcpServers": {
    "warpgbm": {
      "url": "https://warpgbm.ai/mcp/sse"
    }
  }
}

For Developers (REST API)

# 1. Train a model
curl -X POST https://warpgbm.ai/train \
  -H "Content-Type: application/json" \
  -d '{
    "X": [[5.1,3.5,1.4,0.2], [6.7,3.1,4.4,1.4], ...],
    "y": [0, 1, 2, ...],
    "model_type": "warpgbm",
    "objective": "multiclass"
  }'

# Response includes artifact_id for fast predictions
# {"artifact_id": "abc-123", "model_artifact_joblib": "H4sIA..."}

# 2. Make fast predictions (cached, <100ms)
curl -X POST https://warpgbm.ai/predict_from_artifact \
  -H "Content-Type: application/json" \
  -d '{
    "artifact_id": "abc-123",
    "X": [[5.0,3.4,1.5,0.2]]
  }'

🚀 Key Features

| Feature | Description | |---------|-------------| | 🎯 Multi-Model | WarpGBM (GPU) + LightGBM (CPU) | | ⚡ Smart Caching | artifact_id → 5min cache → <100ms inference | | 📦 Portable Artifacts | Download joblib models, use anywhere | | 🤖 MCP Native | Direct tool integration for AI agents | | 💰 X402 Payments | Optional micropayments (Base network) | | 🔒 Stateless | No data storage, you own your models | | 🌐 Production Ready | Deployed on Modal with custom domain |

🐍 Python Package vs MCP Service

This repo is the MCP service wrapper. For production ML workflows, consider using the WarpGBM Python package directly:

| Feature | MCP Service (This Repo) | Python Package | |---------|------------------------|----------------| | Installation | None needed | pip install git+https://... | | GPU | Cloud (pay-per-use) | Your GPU (free) | | Control | REST API parameters | Full Python API | | Features | Train, predict, upload | + Cross-validation, callbacks, feature importance | | Best For | Quick experiments, demos | Production pipelines, research | | Cost | $0.01 per training | Free (your hardware) |

Use this MCP service for: Quick tests, prototyping, agents without local GPU
Use Python package for: Production ML, research, cost savings, full control

📡 Available Endpoints

Core Endpoints

| Method | Endpoint | Description | |--------|----------|-------------| | GET | /models | List available model backends | | POST | /train | Train model, get artifact_id + model | | POST | /predict_from_artifact | Fast predictions (artifact_id or model) | | POST | /predict_proba_from_artifact | Probability predictions | | POST | /upload_data | Upload CSV/Parquet for training | | POST | /feedback | Submit feedback to improve service | | GET | /healthz | Health check with GPU status |

MCP Integration

| Method | Endpoint | Description | |--------|----------|-------------| | SSE | /mcp/sse | MCP Server-Sent Events endpoint | | GET | /.well-known/mcp.json | MCP capability manifest | | GET | /.well-known/x402 | X402 pricing manifest |

💡 Complete Example: Iris Dataset

# 1. Train WarpGBM on Iris (60 samples recommended for proper binning)
curl -X POST https://warpgbm.ai/train \
  -H "Content-Type: application/json" \
  -d '{
  "X": [[5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5],
        [5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5],
        [5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5]],
  "y": [0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,
        0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,
        0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2],
  "model_type": "warpgbm",
  "objective": "multiclass",
  "n_estimators": 100
}'

# Response:
{
  "artifact_id": "abc123-def456-ghi789",
  "model_artifact_joblib": "H4sIA...",
  "training_time_seconds": 0.0
}

# 2. Fast inference with cached artifact_id (<100ms)
curl -X POST https://warpgbm.ai/predict_from_artifact \
  -H "Content-Type: application/json" \
  -d '{
  "artifact_id": "abc123-def456-ghi789",
  "X": [[5,3.4,1.5,0.2], [6.7,3.1,4.4,1.4], [7.7,3.8,6.7,2.2]]
}'

# Response: {"predictions": [0, 1, 2], "inference_time_seconds": 0.05}
# Perfect classification! ✨

⚠️ Important: WarpGBM uses quantile binning which requires 60+ samples for proper training. With fewer samples, the model can't learn proper decision boundaries.

🏠 Self-Hosting

Local Development

# Clone repo
git clone https://github.com/jefferythewind/mcp-warpgbm.git
cd mcp-warpgbm

# Setup environment
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run locally (GPU optional for dev)
uvicorn local_dev:app --host 0.0.0.0 --port 8000 --reload

# Test
curl http://localhost:8000/healthz

Deploy to Modal (Production)

# Install Modal
pip install modal

# Authenticate
modal token new

# Deploy
modal deploy modal_app.py

# Service will be live at your Modal URL

Deploy to Other Platforms

# Docker (requires GPU)
docker build -t warpgbm-mcp .
docker run --gpus all -p 8000:8000 warpgbm-mcp

# Fly.io, Railway, Render, etc.
# See their respective GPU deployment docs

🧪 Testing

# Install dev dependencies
pip install -r requirements-dev.txt

# Run all tests
./run_tests.sh

# Or use pytest directly
pytest tests/ -v

# Test specific functionality
pytest tests/test_train.py -v
pytest tests/test_integration.py -v

📦 Project Structure

mcp-warpgmb/
├── app/
│   ├── main.py              # FastAPI app + routes
│   ├── mcp_sse.py           # MCP Server-Sent Events
│   ├── model_registry.py    # Model backend registry
│   ├── models.py            # Pydantic schemas
│   ├── utils.py             # Serialization, caching
│   ├── x402.py              # Payment verification
│   └── feedback_storage.py  # Feedback persistence
├── .well-known/
│   ├── mcp.json             # MCP capability manifest
│   └── x402                 # X402 pricing manifest
├── docs/
│   ├── AGENT_GUIDE.md       # Comprehensive agent docs
│   ├── MODEL_SUPPORT.md     # Model parameter reference
│   └── WARPGBM_PYTHON_GUIDE.md
├── tests/
│   ├── test_train.py
│   ├── test_predict.py
│   ├── test_integration.py
│   └── conftest.py
├── examples/
│   ├── simple_train.py
│   └── compare_models.py
├── modal_app.py             # Modal deployment config
├── local_dev.py             # Local dev server
├── requirements.txt
└── README.md

💰 Pricing (X402)

Optional micropayments on Base network:

| Endpoint | Price | Description | |----------|-------|-------------| | /train | $0.01 | Train model on GPU, get artifacts | | /predict_from_artifact | $0.001 | Batch predictions | | /predict_proba_from_artifact | $0.001 | Probability predictions | | /feedback | Free | Help us improve! |

Note: Payment is optional for demo/testing. See /.well-known/x402 for details.

🔐 Security & Privacy

✅ Stateless: No training data or models persisted
✅ Sandboxed: Runs in temporary isolated directories
✅ Size Limited: Max 50 MB request payload
✅ No Code Execution: Only structured JSON parameters
✅ Rate Limited: Per-IP throttling to prevent abuse
✅ Read-Only FS: Modal deployment uses immutable filesystem

🌍 Available Models

🚀 WarpGBM (GPU)

  • Acceleration: NVIDIA A10G GPUs
  • Speed: 13× faster than LightGBM
  • Best For: Time-series, financial modeling, temporal data
  • Special: Era-aware splitting, invariant learning
  • Min Samples: 60+ recommended

⚡ LightGBM (CPU)

  • Acceleration: Highly optimized CPU
  • Speed: 10-100× faster than sklearn
  • Best For: General tabular data, large datasets
  • Special: Categorical features, low memory
  • Min Samples: 20+

🗺️ Roadmap

  • [x] Core training + inference endpoints
  • [x] Smart artifact caching (5min TTL)
  • [x] MCP Server-Sent Events integration
  • [x] X402 payment verification
  • [x] Modal deployment with GPU
  • [x] Custom domain (warpgbm.ai)
  • [x] Smithery marketplace listing
  • [ ] ONNX export support
  • [ ] Async job queue for large datasets
  • [ ] S3/IPFS dataset URL support
  • [ ] Python client library (warpgbm-client)
  • [ ] Additional model backends (XGBoost, CatBoost)

💬 Feedback & Support

Help us make this service better for AI agents!

Submit feedback about:

  • Missing features that would unlock new use cases
  • Confusing documentation or error messages
  • Performance issues or timeout problems
  • Additional model types you'd like to see
# Via API
curl -X POST https://warpgbm.ai/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "feedback_type": "feature_request",
    "message": "Add support for XGBoost backend",
    "severity": "medium"
  }'

Or via:

📚 Learn More

📄 License

GPL-3.0 (same as WarpGBM core)

This ensures improvements to the MCP wrapper benefit the community, while allowing commercial use through the cloud service.

🙏 Credits

Built with:

  • WarpGBM - GPU-accelerated GBDT library
  • Modal - Serverless GPU infrastructure
  • FastAPI - Modern Python web framework
  • LightGBM - Microsoft's GBDT library

Built with ❤️ for the open agent economy

⭐ Star on GitHub • 🚀 Try Live Service • 📖 Read the Docs

Quick Setup
Installation guide for this server

Install Package (if required)

uvx warpgbm-mcp-service

Cursor configuration (mcp.json)

{ "mcpServers": { "jefferythewind-warpgbm-mcp-service": { "command": "uvx", "args": [ "warpgbm-mcp-service" ] } } }