​
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

  • 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