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:

npx -y @smithery/cli install @didlawowo/mcp-collection --client claude

Required packages:

datadog-api-client
fastmcp
loguru
icecream
python-dotenv
uv

Environment Setup 🔑

Create a .env file with your Datadog credentials:

DD_API_KEY=your_api_key
DD_APP_KEY=your_app_key

Setup Claude Desktop Setup for MCP 🖥️

Install Claude Desktop

# Assuming you're on macOS
brew install claude-desktop

# Or download from official website
https://claude.ai/desktop

Set up Datadog MCP config:

# 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"
      }
    },

Usage 💻

get logs

get monitor

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

https://medium.com/@pedro.aquino.se/how-to-use-mcp-tools-on-claude-desktop-app-and-automate-your-daily-tasks-1c38e22bc4b0

How it works - Available Functions 🛠️

the LLM use provided function to get the data and use it

1. Get Monitor States

get_monitor_states(
    name: str,           # Monitor name to search
    timeframe: int = 1   # Hours to look back
)

Example:

response = get_monitor_states(name="traefik")

# Sample Output
{
    "id": "12345678",
    "name": "traefik",
    "status": "OK",
    "query": "avg(last_5m):avg:traefik.response_time{*} > 1000",
    "message": "Response time is too high",
    "type": "metric alert",
    "created": "2024-01-14T10:00:00Z",
    "modified": "2024-01-14T15:30:00Z"
}

2. Get Kubernetes Logs

get_k8s_logs(
    cluster: str,            # Kubernetes cluster name
    timeframe: int = 5,      # Hours to look back
    namespace: str = None    # Optional namespace filter
)

Example:

logs = get_k8s_logs(
    cluster="prod-cluster",
    timeframe=3,
    namespace="default"
)

# Sample Output
{
    "timestamp": "2024-01-14T22:00:00Z",
    "host": "worker-1",
    "service": "nginx-ingress",
    "pod_name": "nginx-ingress-controller-abc123",
    "namespace": "default",
    "container_name": "controller",
    "message": "Connection refused",
    "status": "error"
}

# Install as MCP extension
cd datadog
task install-mcp

4. Verify Installation

In Claude chat desktop

check datadog connection in claude

setup 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

# Launch MCP Inspector for debugging
task run-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
Error: (403) Forbidden
➡️ Check your DD_API_KEY and DD_APP_KEY in .env
MCP Connection Issues
Error: Failed to connect to MCP server
➡️ Verify your claude_desktop_config.json path and content
Monitor Not Found
Error: No monitor found with name 'xxx'
➡️ Check monitor name spelling and case sensitivity
logs can be found here

alt text

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

security - not tested

license - not found

quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

provide access to monitor and cluster logs from datadog

Related Resources

Reddit Discussion about this server

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