DataDog MCP Server

# DataDog MCP Server 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 ## 🛠️ 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/)