Build a MCP Server
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A complete walkthrough on how to build an MCP server to serve a trained Random Forest model and integrate it with Bee Framework for ReAct interactivity.
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- Author
- nicknochnack
- GitHub stars
- 57
- Downloads
- 262
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- Developer Tools
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- Serves a trained Random Forest model via MCP.
- Integrates with Bee Framework for ReAct interactivity.
- Uses uv for Python environment and dependency management.
- Provides a complete step-by-step build walkthrough.
- Includes a companion FastAPI ML server for model hosting.
- References building MCP clients for the agent.
Clone the repository, create a virtual environment with uv venv, install dependencies using uv add . and uv add ".[dev]", then start the MCP server with uv run mcp dev server.py. In a separate terminal, run the agent with uv run singleflowagent.py. A separate FastAPI-hosted ML server is also required; clone its repository and run with uvicorn mlapi:app --reload.
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 📺
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