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:

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.

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security – no known vulnerabilities

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quality - confirmed to work

Tools

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

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