MCP Server + Flight + XGBoost

A demonstration of integrating machine learning models with Claude using the Model Control Protocol (MCP) server

Overview

In this example, we’ll create: - A Claude integration with MCP server that can access the sentiment analysis capabilities - A sentiment analysis model server using Arrow Flight using UDFs with xorq - a XGBoost UDF with pre-trained model that will be served by Flight

This pattern enables AI assistants like Claude to access specialized machine learning models while maintaining a conversational interface, expanding Claude’s capabilities beyond its training data.

Demo

Watch this demonstration of the MCP server with Claude in action:

Try It Yourself

The complete source code for this example is available in the xorq GitHub repository: mcp_flight_server.py

How It Works

sequenceDiagram
    participant U as User
    participant C as Claude
    participant MCP as MCP Server
    participant FS as Flight Service
    
    U->>C: "How positive is this headline?"
    C->>MCP: Request sentiment analysis
    Note right of MCP: Input mapping
    MCP->>FS: Transform text and predict
    FS-->>MCP: Return sentiment score
    Note right of MCP: Output mapping
    MCP-->>C: Return formatted results
    C-->>U: "The headline has a sentiment score of 1 (positive)"

  • MCP Server: The example initializes a specialized Flight server with a Model Control Protocol (MCP) server. This provides a standardized way for Claude to interact with the ML model.

  • Flight Service: The server loads two pre-trained models:

    • A TF-IDF vectorizer to transform text data
    • An XGBoost regression model trained to predict sentiment scores

  • Input/Output Mappers: Custom functions translate between Claude’s natural language requests and the ML model’s required format, then transform the model outputs back into a Claude-friendly response.

Next Steps

Try modifying this example to: - Create a more complex input mapping to handle various types of user queries