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BuildMCPServer

by: nicknochnack

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.

22created 13/03/2025
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πŸ“ŒOverview

Purpose: The main goal of the framework is to provide a comprehensive guide for building a Multi-Client-Protocol (MCP) server that serves a trained Random Forest model and integrates with the Bee Framework to enable interactive applications.

Overview: This document outlines a step-by-step process for setting up an MCP server using FastAPI and provides instructions to make the server accessible for machine learning tasks. It also highlights how to integrate the server with interactive tools to enhance user engagement.

Key Features:

  • MCP Server Setup: Detailed instructions on cloning and configuring the MCP server repository, including environment setup and server execution, facilitating easy deployment of machine learning models.

  • Interactivity Integration: Guidance on using the Bee Framework for ReAct interactivity, which enhances the user experience by allowing real-time interactions with the machine learning server.


Build a MCP Server

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.

See it live and in action πŸ“Ί

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

Startup MCP Server πŸš€

  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 πŸ”—

Who, When, Why?

πŸ‘¨πŸΎβ€πŸ’» Author: Nick Renotte
πŸ“… Version: 1.x
πŸ“œ License: This project is licensed under the MIT License