MCP Server for ML Model Integration

# 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 📺 <a href="https://www.linkedin.com/posts/nicholasrenotte_mcp-servers-make-tools-a-bunch-easier-for-activity-7305748751162163200-dIEn?utm_source=share&utm_medium=member_desktop&rcm=ACoAABbxZgUBrud9C531KZPQHCs2riXCiv9Av2A"><img src="https://i.imgur.com/Y2LN9dd.png"/></a> # 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 <a href="https://youtu.be/C82lT9cWQiA?si=dIsL6eM1lUMAVcf0">here</a></br> # Other References 🔗 </br> - <a href="https://github.com/RGGH/mcp-client-x/blob/main/src/client/mcp_client.py">Building MCP Clients (used in singleflow agent)</a></br> - <a href="https://www.youtube.com/watch?v=C82lT9cWQiA&t=1003s ">Original Video where I build the ML server</a> # Who, When, Why? 👨🏾‍💻 Author: Nick Renotte <br /> 📅 Version: 1.x<br /> 📜 License: This project is licensed under the MIT License </br>