Get started with MCP Monitoring

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.

flowchart TB;
    subgraph " "
        direction LR;
        subgraph " "
            direction LR;
            Shield360_SDK[Shield360 SDK] -->|Sends Traces & Metrics| OTC[OpenTelemetry Collector];
            OTC -->|Stores Data| ClickHouseDB[ClickHouse];
        end
        subgraph " "
            direction RL;
            Shield360_UI[Shield360] -->|Pulls Data| ClickHouseDB;
        end
    end

Deploy Shield360

Git clone Shield360 repository

git clone git@github.com:ThinkfleetAI/Shield360.git

Start Docker Compose

From the root directory of the Shield360 distribution, Run the below command:

docker compose up -d

Install Shield360 SDK

Python
Typescript

pip install shield360

npm install shield360

Instrument your MCP server

Python
Typescript

Zero-Code instrumentation
Manual instrumentation

Via CLI arguments
Via environment variables

# Install Shield360
pip install shield360

# Start MCP monitoring instantly
shield360-instrument --service-name my-mcp-app python your_mcp_app.py

# With custom settings for MCP applications
shield360-instrument \
  --otlp-endpoint http://127.0.0.1:4318 \
  --service-name my-mcp-app \
  --environment production \
  python your_mcp_app.py

# Install Shield360
pip install shield360

# 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
shield360-instrument python your_mcp_app.py

Via function parameters
Via environment variables

Add the following two lines to your MCP application code:

import shield360

shield360.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 shield360

shield360.init()

Manual instrumentation

Parameters
Environment variables

Add the following two lines to your MCP application code:

import { Shield360 } from 'shield360';

await Shield360.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 { Shield360 } from 'shield360';

await Shield360.init();

Monitor, debug and test the quality of your MCP server

Navigate to Shield360 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

flowchart TB;
    subgraph " "
        direction LR;
        ApplicationCode[Application Code] -->|Instrumented with| Shield360_SDK[Shield360 SDK];
        Shield360_SDK -->|Sends Traces & Metrics| OT_Backend[OpenTelemetry Backend];
    end

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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