Datadog MCP Server

A Model Context Protocol (MCP) server that enables AI assistants to interact with Datadog's observability platform through natural language.

Features

• Metrics: Query time-series data, list metrics, get metadata
• Logs: Search and filter log events
• APM: Access trace data, service maps, dependencies
• Infrastructure: Host information, container data, process metrics
• Dashboards: List and read dashboard configurations
• Monitors: Alert rules and status information
• Incidents: Incident tracking and management
• Service Catalog: Service definitions and relationships
• SLOs: Service level objectives and compliance data
• Usage: Account usage statistics

Installation

From Source

git clone https://github.com/brukhabtu/datadog-mcp.git
cd datadog-mcp
pip install -e .

Using Docker

docker pull ghcr.io/brukhabtu/datadog-mcp:latest

Configuration

Required Environment Variables

DATADOG_API_KEY="your-datadog-api-key"
DATADOG_APP_KEY="your-datadog-application-key"

Optional Environment Variables

DATADOG_BASE_URL="https://api.datadoghq.com"  # Default US site
DATADOG_TIMEOUT=30                             # Request timeout in seconds
MCP_TRANSPORT=stdio                            # Transport method (stdio/websocket)
MCP_PORT=8000                                  # Port for WebSocket transport
MCP_LOG_LEVEL=INFO                             # Logging level
MCP_ENABLE_SECURITY_FILTERING=true             # Enable read-only filtering

Regional Endpoints

For different Datadog regions:

• US: https://api.datadoghq.com (default)
• EU: https://api.datadoghq.eu
• US3: https://api.us3.datadoghq.com
• US5: https://api.us5.datadoghq.com
• AP1: https://api.ap1.datadoghq.com

Usage

Command Line

# Run with default stdio transport
datadog-mcp

# Run with WebSocket transport
datadog-mcp --transport websocket --port 8000

# Run with debug logging
datadog-mcp --log-level DEBUG

Docker

# Run with environment file
docker run --env-file .env ghcr.io/brukhabtu/datadog-mcp:latest

# Run with individual environment variables
docker run -e DATADOG_API_KEY=your-key \
           -e DATADOG_APP_KEY=your-app-key \
           ghcr.io/brukhabtu/datadog-mcp:latest

Claude Desktop Integration

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Linux: ~/.config/claude/claude_desktop_config.json

{
  "mcpServers": {
    "datadog": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "--env", "DATADOG_API_KEY",
        "--env", "DATADOG_APP_KEY",
        "ghcr.io/brukhabtu/datadog-mcp:latest"
      ],
      "env": {
        "DATADOG_API_KEY": "your-datadog-api-key",
        "DATADOG_APP_KEY": "your-datadog-application-key"
      }
    }
  }
}

Or use the native installation:

{
  "mcpServers": {
    "datadog": {
      "command": "datadog-mcp",
      "args": ["--transport", "stdio"],
      "env": {
        "DATADOG_API_KEY": "your-datadog-api-key",
        "DATADOG_APP_KEY": "your-datadog-application-key"
      }
    }
  }
}

OpenAPI Specification

The server requires the Datadog v2 API OpenAPI specification to be placed at: src/datadog_mcp/specs/datadog-v2.yaml

You can obtain this specification from:

• Datadog's API documentation
• Datadog's GitHub repositories
• Community-maintained specifications

Security

By default, the server runs with security filtering enabled (MCP_ENABLE_SECURITY_FILTERING=true), which restricts operations to read-only access. This includes:

Allowed Operations

• GET requests to query metrics, logs, traces, etc.
• Reading dashboards, monitors, and configurations
• Searching and filtering data
• Viewing usage statistics

Blocked Operations

• All POST, PUT, PATCH, DELETE operations
• User and API key management
• Organization settings modifications
• Any destructive actions

To disable security filtering (not recommended for production):

MCP_ENABLE_SECURITY_FILTERING=false

Example Interactions

Once configured, you can interact with Datadog through natural language:

• "Show me the error rate for my web service over the last hour"
• "List all active monitors that are alerting"
• "Get the CPU usage metrics for production hosts"
• "Show me recent incidents in the platform team"
• "What's our log volume usage this month?"
• "Find traces with high latency in the payment service"
• "Show me the service dependencies for the API gateway"

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/brukhabtu/datadog-mcp.git
cd datadog-mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

Running Tests

pytest tests/

Building Docker Image

docker build -t datadog-mcp:local .

Architecture

The server follows the same architectural patterns as the Jira MCP implementation:

• FastMCP 2.0: Leverages automatic tool generation from OpenAPI specifications
• Security-First: Default read-only access with configurable filtering
• Environment Configuration: All settings via environment variables
• Docker-First: Containerized deployment for consistency
• Transport Flexibility: Supports both stdio and WebSocket transports

Troubleshooting

Authentication Errors

• Ensure both DATADOG_API_KEY and DATADOG_APP_KEY are set correctly
• Verify your keys have the necessary permissions in Datadog
• Check you're using the correct regional endpoint

Connection Issues

• Verify your network can reach the Datadog API
• Check if you need to configure proxy settings
• Ensure the timeout is sufficient for your queries

Missing Tools

• Verify the OpenAPI specification is present in src/datadog_mcp/specs/
• Check the server logs for any specification loading errors
• Ensure the specification version matches your Datadog API version

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

License

MIT License - see LICENSE file for details

Credits

Based on the Jira MCP implementation pattern.