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MCP server · AI

Datadog MCP

by · github.com/Believe-SA/datadog-mcp

This project is an early-stage Model Context Protocol (MCP) server in Go, exposing tools for LLMs to interact with Datadog’s APIs (V1/V2). It provides a generated client base but is not yet a full implementation. Next steps include extending the toolset.

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InstallView source
43Fair

Scored 5 days ago · breakdown

About Datadog MCP

Datadog MCP is an MCP server published by Believe-SA in the AI category: this project is an early-stage Model Context Protocol (MCP) server in Go, exposing tools for LLMs to interact with Datadog’s APIs (V1/V2). It provides a generated client base but is not yet a full implementation. Next steps include extending the toolset.

The repository has 1 stars and 0 forks, with the last commit 5 months ago. Recent commit activity is the strongest signal that reported issues will get attention.

Install

From source
git clone https://github.com/Believe-SA/datadog-mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

Static checks derived from the repository 5 days ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • !License declaredNo license file. Legally you have no right to use it in a product.
  • ✓Tests presentA test suite exists in the repository.
  • ✓Dependencies pinnedA lockfile pins dependency versions.
  • ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

DataDog MCP Server

CI Release Go Report Card codecov

A Model Context Protocol (MCP) server that provides AI assistants with direct access to DataDog's observability platform through a standardized interface.

🎯 Purpose

This server bridges the gap between Large Language Models (LLMs) and DataDog's comprehensive observability platform, enabling AI assistants to:

  • Monitor Infrastructure: Query dashboards, metrics, and host status
  • Manage Events: Create and retrieve events for incident tracking
  • Analyze Data: Access logs, traces, and performance metrics
  • Automate Operations: Interact with monitors, downtimes, and alerts

🔧 What is MCP?

The Model Context Protocol (MCP) is a standardized way for AI assistants to interact with external tools and data sources. Instead of each AI system building custom integrations, MCP provides a common interface that allows LLMs to:

  • Execute tools with structured inputs and outputs
  • Access real-time data from external systems
  • Maintain context across multiple tool calls
  • Provide consistent, reliable integrations

📊 DataDog Platform

DataDog is a leading observability platform that provides:

  • Infrastructure Monitoring: Track server performance, resource usage, and health
  • Application Performance Monitoring (APM): Monitor application performance and user experience
  • Log Management: Centralized logging with powerful search and analysis
  • Real User Monitoring (RUM): Track user interactions and frontend performance
  • Security Monitoring: Detect threats and vulnerabilities across your infrastructure

🚀 Quick Start

  1. Build the server:

    make build

  2. Configure DataDog API:

    export DD_API_KEY="your-datadog-api-key"
export DATADOG_APP_KEY="your-datadog-app-key"  # Optional
export DATADOG_SITE="datadoghq.eu"  # or datadoghq.com

  3. Generate MCP configuration:

    make create-mcp-config

  4. Run the server:

    ./build/datadog-mcp-server

📚 Documentation

🛠️ Available Tools

Currently implemented tools include:

  • Dashboard Management (v1): v1_list_dashboards, v1_get_dashboard
  • Event Management (v1): v1_list_events, v1_create_event
  • Connection Testing (v1): v1_test_connection
  • Monitor Management (v1): (Coming soon)
  • Metrics & Logs (v1): (Coming soon)

All tools are prefixed with their API version (e.g., v1_, v2_) for clear segregation and future v2 API support.

See docs/tools.md for the complete list and implementation status.

🔧 Development

# Install development tools
make install-dev-tools

# Run tests
make test

# Generate API client
make generate

# Split OpenAPI specifications
make split

# Lint OpenAPI specifications
make lint-openapi

# Build and test
make build

OpenAPI Management

The project includes comprehensive tools for managing OpenAPI specifications:

  • Split Specifications: Break down large OpenAPI files into smaller, manageable pieces
  • Spectral Linting: Validate OpenAPI specifications with custom rules and best practices
  • Code Generation: Generate Go client code from OpenAPI specifications
  • Version Support: Separate handling for DataDog API v1 and v2

See OpenAPI Splitting Guide and Spectral Linting Guide for detailed usage.

📚 Resources

README fetched from the repository 5 days ago. The original is authoritative.

Questions

About Datadog MCP

How do I install Datadog MCP?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Datadog MCP safe to use with an AI agent?

Its trust score is 43 out of 100 (fair). It passes 4 of the 6 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Datadog MCP still maintained?

Yes — the last commit was 5 months ago. The repository has 1 stars and 0 forks and 7 open issues.