Datadog MCP Server
This server lets Claude interact with Datadog to query metrics and logs, explore CI/CD pipelines, and inspect monitors, SLOs, service definitions, and teams.
Metrics: Query metrics with filters, aggregation, grouping, and multiple output formats; list available metrics; discover metric fields and field values.
Logs: Search logs with free-text queries, filters, time ranges, and pagination; discover possible values for log fields.
CI/CD: List CI pipelines and extract pipeline fingerprints for repositories/services.
Service Definitions: List all service definitions, retrieve a specific service's definition with schema version and format options.
Monitors: List Datadog monitors with filters by name, tags, monitor tags, and pagination.
SLOs: List Service Level Objectives with query/tag filters and pagination.
Teams: List Datadog teams and their members, with optional detailed output.
Provides comprehensive monitoring capabilities including CI/CD pipeline management, service logs analysis, metrics querying with flexible filtering and aggregation, monitor and SLO management, service definition retrieval, and team management through the Datadog API.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Datadog MCP Server show me the top 5 services by error rate in production for the last hour"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Datadog MCP Server
This repository is archived and no longer maintained. Datadog now provides anofficial Datadog MCP Server. Use the official server for new integrations. Shelf does not plan to maintain or update this implementation.
A Model Context Protocol (MCP) server that provides comprehensive Datadog monitoring capabilities through Claude Desktop and other MCP clients.
Features
This MCP server enables Claude to:
CI/CD Pipeline Management: List CI pipelines, extract fingerprints
Service Logs Analysis: Retrieve and analyze service logs with environment and time filtering
Metrics Monitoring: Query any Datadog metric with flexible filtering, aggregation, and field discovery
Monitoring & Alerting: List and manage Datadog monitors and Service Level Objectives (SLOs)
Service Definitions: List and retrieve detailed service definitions with metadata, ownership, and configuration
Team Management: List teams, view member details, and manage team information
Related MCP server: Datadog MCP Server
Quick Start
Choose your preferred method to run the Datadog MCP server:
π UVX Direct Run (Recommended)
export DD_API_KEY="your-datadog-api-key" DD_APP_KEY="your-datadog-application-key"
# Latest version (HEAD)
uvx --from git+https://github.com/shelfio/datadog-mcp.git datadog-mcp
# Specific version (recommended for production)
uvx --from git+https://github.com/shelfio/datadog-mcp.git@v0.0.5 datadog-mcp
# Specific branch
uvx --from git+https://github.com/shelfio/datadog-mcp.git@main datadog-mcpπ§ UV Quick Run (Development)
export DD_API_KEY="your-datadog-api-key" DD_APP_KEY="your-datadog-application-key"
git clone https://github.com/shelfio/datadog-mcp.git /tmp/datadog-mcp && cd /tmp/datadog-mcp && uv run ddmcp/server.pyπ³ Podman (Optional)
podman run -e DD_API_KEY="your-datadog-api-key" -e DD_APP_KEY="your-datadog-application-key" -i $(podman build -q https://github.com/shelfio/datadog-mcp.git)Method Comparison:
Method | Speed | Latest Code | Setup | Best For |
π UVX Direct Run | β‘β‘β‘ | β (versioned) | Minimal | Production, Claude Desktop |
π§ UV Quick Run | β‘β‘ | β (bleeding edge) | Clone Required | Development, Testing |
π³ Podman | β‘ | β (bleeding edge) | Podman Required | Containerized Environments |
Requirements
For UVX/UV Methods
Python 3.13+
UV package manager (includes uvx)
Datadog API Key and Application Key
For Podman Method
Podman
Datadog API Key and Application Key
Version Management
When using UVX, you can specify exact versions for reproducible deployments:
Version Formats
Latest:
git+https://github.com/shelfio/datadog-mcp.git(HEAD)Specific Tag:
git+https://github.com/shelfio/datadog-mcp.git@v0.0.5Branch:
git+https://github.com/shelfio/datadog-mcp.git@mainCommit Hash:
git+https://github.com/shelfio/datadog-mcp.git@59f0c15
Recommendations
Production: Use specific tags (e.g.,
@v0.0.5) for stabilityDevelopment: Use latest or specific branch for newest features
Testing: Use commit hashes for exact reproducibility
See GitHub releases for all available versions.
Claude Desktop Integration
Using UVX (Recommended)
Add to Claude Desktop configuration:
Latest version (bleeding edge):
{
"mcpServers": {
"datadog": {
"command": "uvx",
"args": ["--from", "git+https://github.com/shelfio/datadog-mcp.git", "datadog-mcp"],
"env": {
"DD_API_KEY": "your-datadog-api-key",
"DD_APP_KEY": "your-datadog-application-key"
}
}
}
}Specific version (recommended for production):
{
"mcpServers": {
"datadog": {
"command": "uvx",
"args": ["--from", "git+https://github.com/shelfio/datadog-mcp.git@v0.0.5", "datadog-mcp"],
"env": {
"DD_API_KEY": "your-datadog-api-key",
"DD_APP_KEY": "your-datadog-application-key"
}
}
}
}For EU region (see Multi-Region Support for other regions):
{
"mcpServers": {
"datadog": {
"command": "uvx",
"args": ["--from", "git+https://github.com/shelfio/datadog-mcp.git", "datadog-mcp"],
"env": {
"DD_API_KEY": "your-datadog-api-key",
"DD_APP_KEY": "your-datadog-application-key",
"DD_SITE": "datadoghq.eu"
}
}
}
}Using Local Development Setup
For development with local cloned repository:
git clone https://github.com/shelfio/datadog-mcp.git
cd datadog-mcpAdd to Claude Desktop configuration:
{
"mcpServers": {
"datadog": {
"command": "uv",
"args": ["run", "ddmcp/server.py"],
"cwd": "/path/to/datadog-mcp",
"env": {
"DD_API_KEY": "your-datadog-api-key",
"DD_APP_KEY": "your-datadog-application-key"
}
}
}
}Installation Options
UVX Installation (Recommended)
Install and run directly from GitHub without cloning:
export DD_API_KEY="your-datadog-api-key"
export DD_APP_KEY="your-datadog-application-key"
# Latest version
uvx --from git+https://github.com/shelfio/datadog-mcp.git datadog-mcp
# Specific version (recommended for production)
uvx --from git+https://github.com/shelfio/datadog-mcp.git@v0.0.5 datadog-mcpDevelopment Installation
For local development and testing:
Clone the repository:
git clone https://github.com/shelfio/datadog-mcp.git cd datadog-mcpInstall dependencies:
uv syncRun the server:
export DD_API_KEY="your-datadog-api-key" export DD_APP_KEY="your-datadog-application-key" uv run ddmcp/server.py
Podman Installation (Optional)
For containerized environments:
podman run -e DD_API_KEY="your-key" -e DD_APP_KEY="your-app-key" -i $(podman build -q https://github.com/shelfio/datadog-mcp.git)Tools
The server provides these tools to Claude:
list_ci_pipelines
Lists all CI pipelines registered in Datadog with filtering options.
Arguments:
repository(optional): Filter by repository namepipeline_name(optional): Filter by pipeline nameformat(optional): Output format - "table", "json", or "summary"
get_pipeline_fingerprints
Extracts pipeline fingerprints for use in Terraform service definitions.
Arguments:
repository(optional): Filter by repository namepipeline_name(optional): Filter by pipeline nameformat(optional): Output format - "table", "json", or "summary"
list_metrics
Lists all available metrics from Datadog for metric discovery.
Arguments:
filter(optional): Filter to search for metrics by tags (e.g., 'aws:', 'env:', 'service:web')limit(optional): Maximum number of metrics to return (default: 100, max: 10000)
get_metrics
Queries any Datadog metric with flexible filtering and aggregation.
Arguments:
metric_name(required): The metric name to query (e.g., 'aws.apigateway.count', 'system.cpu.user')time_range(optional): "1h", "4h", "8h", "1d", "7d", "14d", "30d"aggregation(optional): "avg", "sum", "min", "max", "count"filters(optional): Dictionary of filters to apply (e.g., {'service': 'web', 'env': 'prod'})aggregation_by(optional): List of fields to group results byformat(optional): "table", "summary", "json", "timeseries"
get_metric_fields
Retrieves all available fields (tags) for a specific metric.
Arguments:
metric_name(required): The metric name to get fields fortime_range(optional): "1h", "4h", "8h", "1d", "7d", "14d", "30d"
get_metric_field_values
Retrieves all values for a specific field of a metric.
Arguments:
metric_name(required): The metric namefield_name(required): The field name to get values fortime_range(optional): "1h", "4h", "8h", "1d", "7d", "14d", "30d"
list_service_definitions
Lists all service definitions from Datadog with pagination and filtering.
Arguments:
page_size(optional): Number of service definitions per page (default: 10, max: 100)page_number(optional): Page number for pagination (0-indexed, default: 0)schema_version(optional): Filter by schema version (e.g., 'v2', 'v2.1', 'v2.2')format(optional): Output format - "table", "json", or "summary"
get_service_definition
Retrieves the definition of a specific service with detailed metadata.
Arguments:
service_name(required): Name of the service to retrieveschema_version(optional): Schema version to retrieve (default: "v2.2", options: "v1", "v2", "v2.1", "v2.2")format(optional): Output format - "formatted", "json", or "yaml"
get_service_logs
Retrieves service logs with comprehensive filtering capabilities.
Arguments:
service_name(required): Name of the servicetime_range(required): "1h", "4h", "8h", "1d", "7d", "14d", "30d"environment(optional): "prod", "staging", "backoffice"log_level(optional): "INFO", "ERROR", "WARN", "DEBUG"format(optional): "table", "text", "json", "summary"
list_monitors
Lists all Datadog monitors with comprehensive filtering options.
Arguments:
name(optional): Filter monitors by name (substring match)tags(optional): Filter monitors by tags (e.g., 'env:prod,service:web')monitor_tags(optional): Filter monitors by monitor tags (e.g., 'team:backend')page_size(optional): Number of monitors per page (default: 50, max: 1000)page(optional): Page number (0-indexed, default: 0)format(optional): Output format - "table", "json", or "summary"
list_slos
Lists Service Level Objectives (SLOs) from Datadog with filtering capabilities.
Arguments:
query(optional): Filter SLOs by name or description (substring match)tags(optional): Filter SLOs by tags (e.g., 'team:backend,env:prod')limit(optional): Maximum number of SLOs to return (default: 50, max: 1000)offset(optional): Number of SLOs to skip (default: 0)format(optional): Output format - "table", "json", or "summary"
get_teams
Lists teams and their members.
Arguments:
team_name(optional): Filter by team nameinclude_members(optional): Include member details (default: false)format(optional): "table", "json", "summary"
Examples
Ask Claude to help you with:
"Show me all CI pipelines for the shelf-api repository"
"Get error logs for the content service in the last 4 hours"
"List all available AWS metrics"
"What are the latest metrics for aws.apigateway.count grouped by account?"
"Get all available fields for the system.cpu.user metric"
"List all service definitions in my organization"
"Get the definition for the user-api service"
"List all teams and their members"
"Show all monitors for the web service"
"List SLOs with less than 99% uptime"
"Extract pipeline fingerprints for Terraform configuration"Configuration
Environment Variables
Variable | Description | Required | Default |
| Datadog API Key | Yes | - |
| Datadog Application Key | Yes | - |
| Datadog site/region (see table below) | No |
|
Multi-Region Support
Datadog operates in multiple regions. Set the DD_SITE environment variable to connect to your Datadog region:
Region | DD_SITE Value | Description |
US1 |
| US (default) |
US3 |
| US3 |
US5 |
| US5 |
EU1 |
| Europe |
AP1 |
| Asia Pacific (Japan) |
US1-FED |
| US Government |
Example for EU region:
export DD_SITE="datadoghq.eu"
export DD_API_KEY="your-api-key"
export DD_APP_KEY="your-app-key"
uvx --from git+https://github.com/shelfio/datadog-mcp.git datadog-mcpSee Datadog's Getting Started with Sites for more information.
Obtaining Datadog Credentials
Log in to your Datadog account
Go to Organization Settings β API Keys
Create or copy your API Key (this is your
DD_API_KEY)Go to Organization Settings β Application Keys
Create or copy your Application Key (this is your
DD_APP_KEY)
Note: These are two different keys:
API Key: Used for authentication with Datadog's API
Application Key: Used for authorization and is tied to a specific user account
Available Tools
13 toolsget_logsB
Search and retrieve logs from Datadog with flexible filtering parameters. Similar to get_metrics but for log data.
Inputs: time_range, filters, query, limit, +2 more.
get_logs_field_valuesB
Get possible values for a specific log field to understand filtering options
Inputs: field_name (required), time_range, query, limit, +1 more.
get_metric_fieldsB
Get available fields/tags for a specific metric from Datadog to help with aggregation queries
Inputs: metric_name (required), time_range, format.
get_metric_field_valuesB
Get all possible values for a specific field of a metric from Datadog to discover available dimensions
Inputs: metric_name (required), field_name (required), format.
get_metricsB
Execute metric queries on Datadog. Specify the metric name and optional filters/aggregations to build and execute the query.
Inputs: metric_name (required), time_range, aggregation, filters, +2 more.
get_pipeline_fingerprintsC
Get unique pipeline fingerprints for specific repositories/services
Inputs: repositories (required), pipeline_name, days_back, limit, +1 more.
get_service_definitionB
Retrieve the definition of a specific service from Datadog. Service definitions contain metadata, ownership, and configuration details for individual services.
Inputs: service_name (required), schema_version, format.
get_teamsB
Get Datadog teams and their members
Inputs: team_name, include_members, page_size, page_number, +1 more.
list_ci_pipelinesC
List CI pipelines from Datadog CI Visibility with optional filtering
Inputs: repository, pipeline_name, days_back, limit, +2 more.
list_metricsA
List all available metrics from Datadog. Useful for discovering metrics before querying them with get_metrics.
Inputs: filter, limit, cursor, format.
list_monitorsC
List all monitors from Datadog. Monitors are used for alerting on metrics, logs, and other data.
Inputs: tags, name, monitor_tags, format, +2 more.
list_service_definitionsA
List all service definitions from Datadog. Service definitions describe the structure, ownership, and metadata of services in your organization.
Inputs: page_size, page_number, schema_version, format.
list_slosC
List Service Level Objectives (SLOs) from Datadog. SLOs define service level targets and track performance against those targets.
Inputs: tags, query, limit, offset, +1 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogβ¦
TDQS
Scored across 13 tools
Most tools have distinct purposes, but there is some potential for confusion between get_logs and get_metrics, as both involve data retrieval with filtering, though they target different data types (logs vs. metrics). The get_metric_fields and get_metric_field_values tools are also closely related, but their descriptions clarify the distinction between listing fields and their values.
Tool names follow a consistent verb_noun pattern throughout, with clear prefixes like 'get_' for retrieval actions and 'list_' for listing actions. This consistency makes it easy to predict tool functionality and navigate the set without confusion.
With 13 tools, the count is well-scoped for a Datadog integration, covering key areas like logs, metrics, services, monitors, and SLOs. Each tool appears to serve a specific purpose without unnecessary duplication, fitting the server's monitoring and observability domain appropriately.
The tool set provides good coverage for data retrieval and listing in Datadog's core areas, but there are minor gaps such as the lack of update or delete operations for resources like monitors or SLOs, which might limit full lifecycle management. However, the available tools support essential discovery and querying workflows effectively.
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