A Python-based tool to interact with Datadog API and fetch monitoring data
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-didlawowo-datadog-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Datadog Model Context Protocol (MCP) 🔍 [](https://smithery.ai/server/@didlawowo/mcp-collection) 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](https://smithery.ai/server/@didlawowo/mcp-collection): ```bash npx -y @smithery/cli install @didlawowo/mcp-collection --client claude ``` Required packages: ```text datadog-api-client fastmcp loguru icecream python-dotenv uv ``` ## Environment Setup 🔑 Create a `.env` file with your Datadog credentials: ```env DD_API_KEY=your_api_key DD_APP_KEY=your_app_key ``` ## Setup Claude Desktop Setup for MCP 🖥️ 1. Install Claude Desktop ```bash # Assuming you're on macOS brew install claude-desktop # Or download from official website https://claude.ai/desktop ``` 2. Set up Datadog MCP config: ```bash # on mac is ~/Library/Application\ Support/Claude/claude_desktop_config.json # Add this to your claude config json ```json "Datadog-MCP-Server": { "command": "uv", "args": [ "run", "--with", "datadog-api-client", "--with", "fastmcp", "--with", "icecream", "--with", "loguru", "--with", "python-dotenv", "fastmcp", "run", "/your-path/mcp-collection/datadog/main.py" ], "env": { "DD_API_KEY": "xxxx", "DD_APP_KEY": "xxx" } }, ``` …
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.