Datadog MCP Servers

Datadog

Integrate with the Datadog API to manage incidents, monitors, logs, dashboards, metrics, traces, and hosts.

Datadog

Enables interaction with the Datadog API using the Model Context Protocol for accessing monitoring data, dashboards, metrics, events, logs, and incidents.

Datadog

Fetches monitoring data from Datadog using a Python interface.

Datadog

Empowers AI assistants with comprehensive access to Datadog observability data, enabling automated monitoring, analysis, and management.

Datadog

Connects Large Language Models with DataDog's observability platform to enable AI assistants to interact with monitoring and operational data.

Domain

Clones and analyzes microservices (code + documentation) to extract business logic, APIs, and database models, storing them in PostgreSQL for fast, structured querying.

Datadog Trace Diagnostic

Exposes Datadog APM, logs, and metrics via Model Context Protocol for AI-driven debugging and observability.

Datadog Playcourt

Integrates comprehensive Datadog monitoring for Kubernetes, APM, and infrastructure metrics within the MCP ecosystem.

Go

Provides a Go package for building MCP (Model Context Protocol) servers with structured logging, file-based configuration, and multiple transport support.

Datadog

Enables AI assistants to interact with Datadog's observability platform using natural language queries.

Datadog

Integrates Datadog APIs into a Model Context Protocol host, enabling AI models to interact with Datadog services.

Datadog

Facilitates the integration of Monitoring Console Protocol (MCP) with Datadog.