MCP Server for ML Model Integration
# 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 šŗ
<a href="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"><img src="https://i.imgur.com/Y2LN9dd.png"/></a>
# Startup MCP Server š
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 <a href="https://youtu.be/C82lT9cWQiA?si=dIsL6eM1lUMAVcf0">here</a></br>
# Other References š </br>
- <a href="https://github.com/RGGH/mcp-client-x/blob/main/src/client/mcp_client.py">Building MCP Clients (used in singleflow agent)</a></br>
- <a href="https://www.youtube.com/watch?v=C82lT9cWQiA&t=1003s ">Original Video where I build the ML server</a>
# Who, When, Why?
šØš¾āš» Author: Nick Renotte <br />
š
Version: 1.x<br />
š License: This project is licensed under the MIT License </br>TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.
With only one tool, naming consistency is inherently perfect, as there are no other tool names to be inconsistent with. The tool name 'PredictChurn' follows a clear verb_noun pattern.
A single tool is too few for a server described as 'ML Model Integration', which suggests a broader scope covering multiple models or operations. This feels thin and incomplete for the apparent domain.
The server is severely incomplete for ML model integration, as it only offers a churn prediction tool. There are significant gaps, such as no tools for training models, listing available models, updating models, or handling other common ML tasks.