Datadog MCP Server
Datadog operates a first-party remote MCP server that bridges AI agents to Datadog observability data. It is not APM-specific — it exposes most Datadog products — but it ships a dedicated `apm` toolset for span search, trace queries, Watchdog stories, latency-bottleneck analysis and APM recommendations, which is the agent-facing counterpart of the REST surface captured in openapi/.
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Descriptor
Candidate descriptor · transport streamable-http
Tools
apm_search_spans— Searches for spans using APM query syntax, with support for pagination and tag filtering.apm_query_trace— Queries a trace's span data to filter, aggregate, or rank spans, such as finding the highest self-time spans or tracing an error to its origin service.apm_discover_span_tags— Discovers available tag keys on spans within a time range.apm_get_primary_tag_keys— Retrieves the primary tag keys configured for the organization.apm_search_watchdog_stories— Searches for Watchdog anomaly detection stories for a service within a time range, providing AI-powered insights into latency, error rate, and traffic anomalies.apm_get_watchdog_story— Retrieves detailed information about a specific Watchdog story by its ID.apm_latency_bottleneck_summary— Analyzes latency bottlenecks across traces in an anomaly period using self-time calculations, detects cascading call patterns, and surfaces root causes of latency spikes.get_change_stories— Retrieves change stories from the Change Tracking API for APM services — deployments, feature flags, configuration updates and infrastructure events in a time range.semantic_search_change_stories— Searches change stories using natural language and AI-powered semantic search.apm_search_recommendations— Searches for APM recommendations from Datadog.apm_get_recommendation— Retrieves full details of a specific APM recommendation by ID.get_datadog_trace— Core-toolset trace retrieval, available without the apm preview toolset.search_datadog_spans— Core-toolset span search.search_datadog_services— Core-toolset service search.search_datadog_service_dependencies— Core-toolset service dependency search.search_datadog_slos— Searches Datadog SLOs by name, tags, or type, with query syntax for service/team filtering.
About MCP
The Model Context Protocol (MCP) is an open protocol Anthropic introduced for connecting LLM-based agents to external tools and data sources. Providers publish MCP servers that expose their API surface as structured, discoverable tools — an MCP-compatible client (Claude Desktop, Cursor, Cline, Continue, etc.) can connect to the server and call its tools without any per-provider integration code.
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