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.
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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.
git clone https://github.com/nicknochnack/BuildMCPServercd BuildMCPServeruv venvsource .venv/bin/activateuv add .uv add ".[dev]"uv run mcp dev server.pysource .venv/bin/activateuv run singleflowagent.pygit 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
👨🏾💻 Author: Nick Renotte 📅 Version: 1.x 📜 License: This project is licensed under the MIT License
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