About Build a MCP Server
A tutorial project showing how to build an MCP server that serves a trained Random Forest model and integrates it with Bee Framework for ReAct interaction. It is aimed at developers who want to expose ML models through MCP.
Key features
- 01MCP server serving a Random Forest model
- 02ReAct interaction integrated with Bee Framework
- 03Run server and agent scripts with uv
- 04Includes setup notes for a FastAPI-hosted ML server
Use cases
- 01Learning how to wrap an ML model as an MCP server
- 02Prototyping a ReAct agent built on Bee Framework
MCP support
The project provides an MCP server that exposes the trained model, and a separate agent script that uses an MCP client to call it.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEBuild 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 🚀
- Clone this repo
git clone https://github.com/nicknochnack/BuildMCPServer - To run the MCP server
cd BuildMCPServer
uv venv
source .venv/bin/activate
uv add .
uv add ".[dev]"
uv run mcp dev server.py - 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 🔗
Who, When, Why?
👨🏾💻 Author: Nick Renotte
📅 Version: 1.x
📜 License: This project is licensed under the MIT License
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.