TANTIOPE/datadog-mcp-server

Overview

A TypeScript MCP server that gives AI assistants read and write access to Datadog observability data. It exposes Datadog's API through the Model Context Protocol, enabling automated monitoring queries and incident management.

Best for

Best for
Teams using Datadog who want to automate observability tasks through AI assistants

Use cases

• Query Datadog metrics and logs from an AI coding assistant
• Create and update Datadog monitors programmatically
• Trigger incident responses based on AI analysis of observability data

How to use

Tools exposed

• monitors
• dashboards
• logs
• logs_pipelines
• logs_indexes
• synthetics
• rum
• security
• apm
• metrics

Tested with

Claude Desktop, VS Code, Cursor

Example client config

{\n  "mcpServers": {\n    "datadog": {\n      "command": "npx",\n      "args": ["-y", "datadog-mcp"],\n      "env": {\n        "DD_API_KEY": "your-api-key",\n        "DD_APP_KEY": "your-app-key"\n      }\n    }\n  }\n}

Notes

A TypeScript MCP server that gives AI assistants read and write access to Datadog observability data. It exposes Datadog’s API through the Model Context Protocol, enabling automated monitoring queries and incident management.

4 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Query Datadog metrics and logs from an AI coding assistant
• Create and update Datadog monitors programmatically
• Trigger incident responses based on AI analysis of observability data

Pros

• Direct integration with Datadog’s full API surface
• TypeScript codebase is easy to extend or audit
• Low overhead for teams already using MCP-based tools

Cons

• Requires Datadog API keys and proper permissions to configure
• Limited to Datadog’s API rate limits and data retention policies
• No built-in caching or batching for high-frequency queries

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.