# DataDog MCP Server

[![CI](https://github.com/chussenot/datadog-mcp/actions/workflows/ci.yml/badge.svg)](https://github.com/chussenot/datadog-mcp/actions/workflows/ci.yml)
[![Release](https://github.com/chussenot/datadog-mcp/actions/workflows/release.yml/badge.svg)](https://github.com/chussenot/datadog-mcp/actions/workflows/release.yml)
[![Go Report Card](https://goreportcard.com/badge/github.com/chussenot/datadog-mcp)](https://goreportcard.com/report/github.com/chussenot/datadog-mcp)
[![codecov](https://codecov.io/gh/chussenot/datadog-mcp/branch/main/graph/badge.svg)](https://codecov.io/gh/chussenot/datadog-mcp)

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**:

   ```bash
   make build
   ```

2. **Configure DataDog API**:

   ```bash
   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**:

   ```bash
   make create-mcp-config
   ```

4. **Run the server**:

   ```bash
   ./build/datadog-mcp-server
   ```

## 📚 Documentation

- **[Available Tools](docs/tools.md)** - Complete list of implementable DataDog tools
- **[Test Documentation](docs/tests.md)** - Test coverage and implementation details
- **[OpenAPI Splitting](docs/openapi-splitting.md)** - How to split large OpenAPI specifications
- **[Spectral Linting](docs/spectral-linting.md)** - OpenAPI specification validation and linting
- **[GitHub Actions](docs/github-actions.md)** - CI/CD pipeline 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](docs/tools.md) for the complete list and implementation status.

## 🔧 Development

```bash
# 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](docs/openapi-splitting.md) and [Spectral Linting Guide](docs/spectral-linting.md) for detailed usage.

## 📚 Resources

- [Model Context Protocol Introduction (Stytch Blog)](https://stytch.com/blog/model-context-protocol-introduction/)