ppandrangi/datadog-mcp
If you are the rightful owner of datadog-mcp and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
The Datadog MCP Server is a comprehensive Model Context Protocol server designed for seamless integration with Datadog, offering full CRUD access to Datadog APIs using modern asynchronous patterns.
Datadog MCP Server
A comprehensive Model Context Protocol (MCP) server for Datadog integration, providing full CRUD access to Datadog APIs with modern async patterns. Built with the official Datadog Python SDK and MCP Python SDK.
🚀 Features
- 🔧 Full CRUD operations - Create, read, update, delete across all supported APIs
- ⚡ Async operations - Built with AsyncApiClient for optimal performance
- 🔄 Automatic retries - Rate limiting and error handling with exponential backoff
- 📊 Comprehensive coverage - 31 tools across all major Datadog APIs
- 💾 Local caching - Results stored as timestamped JSON files
- 🔒 Type-safe - Full type hints and Pydantic models
- 📈 Built-in analysis - Statistical analysis, trend detection, and data summarization
- 🛡️ Security-first - Environment-based credential management
📋 Prerequisites
- Python 3.8+
- Valid Datadog API and Application keys
- MCP-compatible client (VS Code, Cursor, Claude Desktop, etc.)
Quick Start
# Install dependencies
pip install -r requirements.txt
# Set environment variables
export DATADOG_API_KEY="your_api_key"
export DATADOG_APP_KEY="your_app_key"
export DATADOG_SITE="datadoghq.com" # Optional
# Run the server
python server.py
MCP Client Integration
VS Code with Continue
- Install the Continue extension in VS Code
- Add to your Continue config (
~/.continue/config.json
):
{
"mcpServers": {
"datadog": {
"command": "python",
"args": ["/path/to/datadog-mcp-python/server.py"],
"env": {
"DATADOG_API_KEY": "your_api_key",
"DATADOG_APP_KEY": "your_app_key"
}
}
}
}
Cursor
- Open Cursor settings
- Add MCP server configuration:
{
"mcp.servers": {
"datadog": {
"command": "python",
"args": ["/path/to/datadog-mcp-python/server.py"],
"env": {
"DATADOG_API_KEY": "your_api_key",
"DATADOG_APP_KEY": "your_app_key"
}
}
}
}
Amazon Q Developer
- Configure in your Q Developer settings:
{
"mcpServers": {
"datadog-mcp": {
"command": "python3",
"args": ["/path/to/datadog-mcp-python/server.py"],
"env": {
"DATADOG_API_KEY": "your_api_key",
"DATADOG_APP_KEY": "your_app_key",
"FASTMCP_LOG_LEVEL": "ERROR"
},
"disabled": false,
"autoApprove": []
}
}
}
Claude Desktop
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json
on macOS):
{
"mcpServers": {
"datadog": {
"command": "python",
"args": ["/path/to/datadog-mcp-python/server.py"],
"env": {
"DATADOG_API_KEY": "your_api_key",
"DATADOG_APP_KEY": "your_app_key"
}
}
}
}
Gemini CLI
- Install Gemini CLI with MCP support
- Configure the server:
gemini mcp add datadog python /path/to/datadog-mcp-python/server.py \
--env DATADOG_API_KEY=your_api_key \
--env DATADOG_APP_KEY=your_app_key
Generic MCP Client
For any MCP-compatible client, use these connection details:
- Transport: stdio
- Command:
python server.py
- Working Directory:
/path/to/datadog-mcp-python/
- Environment Variables:
DATADOG_API_KEY
,DATADOG_APP_KEY
Available Tools (31 Total)
Metrics & Monitoring (9 tools)
validate_api_key
- Test API credentialsget_metrics
- Query time series datasearch_metrics
- Find metrics by patternget_metric_metadata
- Get metric metadataget_monitors
- List monitoring alertsget_monitor
- Get specific monitor detailscreate_monitor
- Create new monitoring alertsupdate_monitor
- Update existing monitorsdelete_monitor
- Delete monitors
Dashboards & Visualization (5 tools)
get_dashboards
- List all dashboardsget_dashboard
- Get dashboard detailscreate_dashboard
- Create new dashboardsupdate_dashboard
- Update existing dashboardsdelete_dashboard
- Delete dashboards
Logs & Events (2 tools)
search_logs
- Search log entriesget_events
- Get system events
Infrastructure & Tags (5 tools)
get_infrastructure
- Get host informationget_service_map
- Get service dependenciesget_tags
- Get host tagsget_downtimes
- Get scheduled downtimescreate_downtime
- Create scheduled downtimes
Testing & Applications (2 tools)
get_synthetics_tests
- Get synthetic testsget_rum_applications
- Get RUM applications
Security & Incidents (4 tools)
get_security_rules
- Get security monitoring rulesget_incidents
- Get incident data (with pagination)get_slos
- Get Service Level Objectivesget_notebooks
- Get Datadog notebooks
Teams & Users (2 tools)
get_teams
- Get teamsget_users
- Get users
Utilities (2 tools)
analyze_data
- Analyze cached datacleanup_cache
- Clean old cache files
Usage Examples
Once connected to an MCP client, you can use natural language to interact with Datadog:
Monitoring Examples
- "Show me all monitors that are currently alerting"
- "Create a monitor for high CPU usage above 80%"
- "Get metrics for system.cpu.user over the last hour"
- "Search for all memory-related metrics"
Dashboard Examples
- "List all my dashboards"
- "Create a new dashboard for system monitoring"
- "Show me the widgets in my main dashboard"
Infrastructure Examples
- "Show me all hosts and their status"
- "Get the service map for my application"
- "List all tags for production hosts"
Incident Management
- "Show me all active incidents"
- "Get the latest security monitoring rules"
- "List all SLOs and their current status"
Configuration
The server uses the latest Datadog API client with:
- AsyncApiClient for non-blocking operations
- Automatic retry on rate limits (429 errors)
- 3 retry attempts with exponential backoff
- Unstable operations enabled for pagination
🏗️ Architecture
Core Components
- DatadogMCPServer: Main server class with API client management
- DatadogConfig: Pydantic model for configuration validation
- Tool Handlers: Individual async functions for each API endpoint
- Data Storage: Automatic JSON file caching with timestamps
- Analysis Engine: Built-in data analysis capabilities
Data Flow
- Request: MCP client calls tool with parameters
- API Call: Server makes authenticated request to Datadog API
- Storage: Response data is cached to local JSON file
- Analysis: Optional built-in analysis of the data
- Response: Summary and file path returned to client
📈 Performance
Async Implementation
- All API calls are asynchronous
- Non-blocking file I/O operations
- Efficient memory usage for large datasets
Rate Limiting
- Respects Datadog API rate limits
- Automatic retry logic with exponential backoff
- Efficient batching for bulk operations
Example Code Usage
# Create a monitor
create_monitor(
name="High CPU Usage",
monitor_type="metric alert",
query="avg(last_5m):avg:system.cpu.user{*} > 0.8",
message="CPU usage is high @slack-alerts"
)
# Create a dashboard
create_dashboard(
title="System Overview",
layout_type="ordered",
widgets=[{
"definition": {
"type": "timeseries",
"requests": [{"q": "avg:system.cpu.user{*}"}]
}
}]
)
# Schedule downtime
create_downtime(
scope=["host:web-server-01"],
start=1640995200,
end=1640998800,
message="Scheduled maintenance"
)
Security & Features
- Full CRUD operations - Complete create, read, update, delete support
- Write operations enabled - All mutation tools available
- Local data caching - All results stored locally as JSON files
- Error handling - Comprehensive exception management
- Pagination support - Handle large datasets efficiently
- Type safety - Full type hints throughout
- Rate limiting - Automatic retry on API limits
Development
Setup
# Install development dependencies
pip install -r requirements.txt
pip install pytest pytest-cov black flake8 mypy
# Format code
black server.py
flake8 server.py --max-line-length=88
# Run tests
cd tests && python -m pytest --cov=../server
Adding New Tools
- Add new method to
DatadogMCPServer
class - Decorate with
@self.mcp.tool()
- Implement proper error handling and data storage
- Add tests and update documentation
Troubleshooting
Common Issues
- Authentication Error: Verify your
DATADOG_API_KEY
andDATADOG_APP_KEY
are correct - Connection Issues: Ensure the server is running and accessible
- Permission Errors: Check that your API keys have the necessary permissions
- Rate Limiting: The server automatically handles rate limits with retries
Debug Mode
Enable debug logging by setting:
export DATADOG_DEBUG=true
License
MIT License - see LICENSE file for details.