datadog
Datadog Model Context Protocol (MCP) 🔍
A Python-based tool to interact with Datadog API and fetch monitoring data from your infrastructure. This MCP provides easy access to monitor states and Kubernetes logs through a simple interface.
Datadog Features 🌟
- Monitor State Tracking: Fetch and analyze specific monitor states
- Kubernetes Log Analysis: Extract and format error logs from Kubernetes clusters
Prerequisites 📋
- Python 3.11+
- Datadog API and Application keys (with correct permissions)
- Access to Datadog site
Installation 🔧
Installing via Smithery
To install Datadog for Claude Desktop automatically via Smithery:
Required packages:
Environment Setup 🔑
Create a .env
file with your Datadog credentials:
Setup Claude Desktop Setup for MCP 🖥️
- Install Claude Desktop
- Set up Datadog MCP config:
Usage 💻
Architecture 🏗
- FastMCP Base: Utilizes FastMCP framework for tool management
- Modular Design: Separate functions for monitors and logs
- Type Safety: Full typing support with Python type hints
- API Abstraction: Wrapped Datadog API calls with error handling
I'll add a section about MCP and Claude Desktop setup:
Model Context Protocol (MCP) Introduction 🤖
What is MCP?
Model Context Protocol (MCP) is a framework allowing AI models to interact with external tools and APIs in a standardized way. It enables models like Claude to:
- Access external data
- Execute commands
- Interact with APIs
- Maintain context across conversations
some examples of MCP servers
https://github.com/punkpeye/awesome-mcp-servers?tab=readme-ov-file
Tutorial for setup MCP
How it works - Available Functions 🛠️
the LLM use provided function to get the data and use it
1. Get Monitor States
Example:
2. Get Kubernetes Logs
Example:
4. Verify Installation
In Claude chat desktop
check datadog connection in claude
5. Use Datadog MCP Tools
Security Considerations 🔒
- Store API keys in
.env
- MCP runs in isolated environment
- Each tool has defined permissions
- Rate limiting is implemented
Troubleshooting 🔧
Using MCP Inspector
The MCP Inspector provides:
- Real-time view of MCP server status
- Function call logs
- Error tracing
- API response monitoring
Common issues and solutions
- API Authentication Errors➡️ Check your DD_API_KEY and DD_APP_KEY in .envCopy
- MCP Connection Issues➡️ Verify your claude_desktop_config.json path and contentCopy
- Monitor Not Found➡️ Check monitor name spelling and case sensitivityCopy
- logs can be found here
Contributing 🤝
Feel free to:
- Open issues for bugs
- Submit PRs for improvements
- Add new features
Notes 📝
- API calls are made to Datadog EU site
- Default timeframe is 1 hour for monitor states
- Page size limits are set to handle most use cases
This server cannot be installed
provide access to monitor and cluster logs from datadog
- Datadog Features 🌟
- Prerequisites 📋
- Installation 🔧
- Environment Setup 🔑
- Setup Claude Desktop Setup for MCP 🖥️
- Usage 💻
- Architecture 🏗
- Model Context Protocol (MCP) Introduction 🤖