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.
πOverview
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:
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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.
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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 πΊ
Startup MCP Server π
- 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 π
Who, When, Why?
π¨πΎβπ» Author: Nick Renotte
π
Version: 1.x
π License: This project is licensed under the MIT License