mcpcat 0.1.0b1

Analytics Tool for MCP Servers - provides insights into MCP tool usage patterns

MCPCat Python SDK

Analytics tool for MCP (Model Context Protocol) servers that provides insights into tool usage patterns.

Features

• Tool Usage Analytics: Tracks which tools are called and how frequently
• Context Injection: Adds context parameters to tools to understand user intent
• Session Tracking: Identifies and tracks user sessions
• Report Missing Tools: Allows clients to report when needed tools are missing
• PII Redaction: Automatically redacts sensitive information from logs
• Non-invasive Integration: Simple one-line integration with existing MCP servers

Compatibility

MCPCat officially supports >=v1.2.0 of the Python SDK. Support for >=1.0.0 is experimental.

Installation

pip install mcpcat

Quick Start

from fastmcp import FastMCP
from mcpcat import track

# Create your MCP server
mcp = FastMCP("my-server")

# Add your tools
@mcp.tool()
def my_tool(arg: str) -> str:
    return f"Result: {arg}"

# Enable MCPCat tracking
track(mcp)

# Run the server
mcp.run()

Configuration

MCPCat can be configured with various options:

from mcpcat import track, MCPCatOptions

options = MCPCatOptions(
    enableToolCallContext=True,   # Add context parameters to tools
    enableTracing=True,           # Trace tool calls
    enableReportMissing=True,     # Add report_missing tool
    identify=my_identify_func     # Custom session identification
)

track(mcp, options)

How It Works

1. MCPCat intercepts the MCP server's tool listing and calling mechanisms
2. It injects a context parameter into each tool's schema
3. When tools are called, it captures analytics data including timing, arguments, and results
4. The report_missing tool allows LLMs to report when they need functionality that isn't available

Custom Session Identification

You can provide a custom function to identify users:

def identify_user(request_context):
    # Your logic to identify the user
    return {
        "sessionId": "session-123",
        "userId": "user-456"
    }

options = MCPCatOptions(identify=identify_user)

Log Format

MCPCat logs are written in JSON format with the following structure:

{
  "timestamp": "2024-01-20T10:30:00Z",
  "event": "tool_call",
  "tool_name": "my_tool",
  "session_id": "session-123",
  "user_id": "user-456",
  "duration": 0.123,
  "result": "success",
  "context_provided": true
}

Development

To set up for development:

# Clone the repository
git clone https://github.com/yourusername/mcpcat-python-sdk.git
cd mcpcat-python-sdk

# Install dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run type checking
mypy src

# Run linting
ruff check src

License

MIT