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MCP Server for ML Model Integration

MCP Server for ML Model Integration

Public
nicknochnack/BuildMCPServer

Serve a trained Random Forest model with a Model Context Protocol (MCP) server featuring seamless integration with the Bee Framework for interactive ReAct capabilities and efficient ML model deployment.

python
0 tools
May 30, 2025
Updated Jun 4, 2025

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Build a MCP Server

A complete walkthrough on how to build a MCP server to serve a trained Random Forest model and integrate it with Bee Framework for ReAct interactivity.

See it live and in action 📺

Startup MCP Server 🚀

  1. Clone this repo git clone https://github.com/nicknochnack/BuildMCPServer
  2. To run the MCP server
    cd BuildMCPServer
    uv venv
    source .venv/bin/activate
    uv add .
    uv add ".[dev]"
    uv run mcp dev server.py
  3. To run the agent, in a separate terminal, run:
    source .venv/bin/activate
    uv run singleflowagent.py

Startup FastAPI Hosted ML Server

git clone https://github.com/nicknochnack/CodeThat-FastML
cd CodeThat-FastML
pip install -r requirements.txt
uvicorn mlapi:app --reload
Detailed instructions on how to build it can also be found here

Other References 🔗

  • Building MCP Clients (used in singleflow agent)
  • Original Video where I build the ML server

Who, When, Why?

👨🏾‍💻 Author: Nick Renotte 📅 Version: 1.x 📜 License: This project is licensed under the MIT License

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