Integrates with a FastAPI hosted ML server to serve a trained Random Forest model for predictions and data processing.
Provides integration with GitHub repositories for cloning and accessing code resources needed for the MCP server setup.
Integrates with Imgur for image hosting used in the demonstration of the MCP server capabilities.
References YouTube tutorials for additional implementation details on building the ML server component.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Server for ML Model Integrationpredict customer churn risk using the latest sales data"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 📺
Startup MCP Server 🚀
Clone this repo
git clone https://github.com/nicknochnack/BuildMCPServerTo run the MCP server
cd BuildMCPServeruv venvsource .venv/bin/activateuv add .uv add ".[dev]"uv run mcp dev server.pyTo run the agent, in a separate terminal, run:
source .venv/bin/activateuv run singleflowagent.py
Startup FastAPI Hosted ML Server
git clone https://github.com/nicknochnack/CodeThat-FastMLcd CodeThat-FastMLpip install -r requirements.txtuvicorn mlapi:app --reload
Detailed instructions on how to build it can also be found here
Other References 🔗
Building MCP Clients (used in singleflow agent)
Original Video where I build the ML server
Who, When, Why?
👨🏾💻 Author: Nick Renotte 📅 Version: 1.x 📜 License: This project is licensed under the MIT License
Resources
Looking for Admin?
Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.