Get started with MCP Monitoring

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.

Deploy OpenLIT

Git clone OpenLIT repository

git clone git@github.com:openlit/openlit.git

Start Docker Compose

From the root directory of the OpenLIT Repo, Run the below command:

docker compose up -d

Install OpenLIT SDK

Want zero-code observability? Try the OpenLIT Controller

The Controller uses eBPF to automatically discover and instrument LLM traffic across Kubernetes, Docker, and Linux — no SDK required. Use SDKs for deeper application-level tracing.

Python
Typescript

pip install openlit

npm install openlit

Instrument your MCP server

Python
Typescript

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

Zero-Code instrumentation
Manual instrumentation

Via CLI arguments
Via environment variables

# 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

# 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

Via function parameters
Via environment variables

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

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()

Manual instrumentation

Parameters
Environment variables

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

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();

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 backendsIf 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.

Quickstart: LLM Evaluations

Get started with evaluating your LLM responses in 2 simple steps

Integrations

60+ AI integrations with automatic instrumentation and performance tracking

Destinations

Send telemetry to Datadog, Grafana, New Relic, and other observability stacks

Zero-code observability with the OpenLIT Controller

Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.