Get started with MCP Monitoring

Quickly start monitoring your MCP (Model Context Protocol) Applications in just a single line of code

OpenLIT automatically instruments MCP alongside LLMs, VectorDBs, and frameworks by default.

This guide demonstrates production-ready MCP (Model Context Protocol) observability setup with OpenTelemetry-native auto-instrumentations. Get enterprise-grade AI monitoring with zero code changes using our CLI or minimal SDK integration for complete MCP performance tracking.

Learn how to implement real-time context tracking, tool usage monitoring, protocol performance analysis, and resource utilization optimization for your MCP applications with OpenTelemetry traces and metrics.

1. Deploy OpenLIT
2. Install OpenLIT SDK

   Python

   pip install openlit
   

   Typescript

   npm install openlit
   
3. Instrument your MCP server

   Python

   Not sure which method to choose? Check out Instrumentation Methods to understand the differences.

   Zero-Code instrumentation

   Via CLI arguments

   # Install OpenLIT
   pip install openlit
   
   # Start MCP monitoring instantly
   openlit-instrument --service-name my-mcp-app python your_mcp_app.py
   
   # With custom settings for MCP applications
   openlit-instrument \
     --otlp-endpoint http://127.0.0.1:4318 \
     --service-name my-mcp-app \
     --environment production \
     python your_mcp_app.py
   

   Via environment variables

   # Install OpenLIT
   pip install openlit
   
   # Set environment variables
   export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
   export OTEL_SERVICE_NAME=my-mcp-app
   export OTEL_DEPLOYMENT_ENVIRONMENT=production
   
   # Run your MCP application
   openlit-instrument python your_mcp_app.py
   

   Manual instrumentation

   Via function parameters

   Add the following two lines to your MCP application code:

   import openlit
   
   openlit.init(
     otlp_endpoint="http://127.0.0.1:4318",
     service_name="my-mcp-app",
     environment="production"
   )
   
   # Your existing MCP code works the same
   # MCP interactions are traced automatically
   

   Via environment variables

   Configure your OTLP endpoint using environment variables:

   export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
   export OTEL_SERVICE_NAME=my-mcp-app
   export OTEL_DEPLOYMENT_ENVIRONMENT=production
   

   Add the following two lines to your MCP application code:

   import openlit
   
   openlit.init()
   

   Typescript

   Manual instrumentation

   Parameters

   Add the following two lines to your MCP application code:

   import { OpenLIT } from 'openlit';
   
   await OpenLIT.init({
     otlpEndpoint: 'http://127.0.0.1:4318',
     serviceName: 'my-mcp-app',
     environment: 'production'
   });
   
   // Your existing MCP code works the same
   // MCP interactions are traced automatically
   

   Environment variables

   Configure your OTLP endpoint using environment variables:

   export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
   export OTEL_SERVICE_NAME=my-mcp-app
   export OTEL_DEPLOYMENT_ENVIRONMENT=production
   

   Add the following two lines to your MCP application code:

   import { OpenLIT } from 'openlit';
   
   await OpenLIT.init();
   
4. Monitor, debug and test the quality of your MCP server

   Navigate to OpenLIT at 127.0.0.1:3000 to start monitoring your MCP applications.

   

   You should see MCP-specific traces and metrics including:

   • Context Protocol Interactions: Track context loading, management, and utilization
   • Tool Usage Metrics: Monitor tool calls and their performance within MCP workflows
   • Protocol Performance: Analyze MCP handshakes and communication efficiency
   • Resource Utilization: Monitor context window usage and memory consumption
   • Error Tracking: Identify and debug MCP protocol errors and failures

   Send Observability telemetry to other OpenTelemetry backends

   If you wish to send telemetry directly from the SDK to another backend, you can stop the current Docker services by using the command below. For more details on sending the data to your existing OpenTelemetry backends, checkout our Supported Destinations guide.

   docker compose down
   

   If you have any questions or need support, reach out to our community.

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