Datadog MCP

Returns profiled services along with their profiling families that can be queried for profiling data in the given query context. Note: results are in no particular order and do not indicate relative importance or activity level of services.

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Discover available tag names (e.g. service, host, env, version, family, runtime-id) from the profile metadata track. Use this to find what tags are available for filtering profiling data. Common infrastructure tags include pod_name, container_id, container_name, availability-zone, datacenter, kube_deployment, kube_nam…

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Get the values for a specific tag (e.g. service, host, env, version, family, runtime-id) from the profile metadata track. Use get_profiling_tag_names first to discover available tags. Returns at most 50 results sorted by frequency. Use the valueSearch parameter to narrow down results when looking for a specific value.

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Query profiling data as timeseries, aggregated into time buckets. Use this to analyze trends, compare services, or identify performance regressions over time. Well suited for cross-service investigations — use groupBy to group by different fields, e.g. service or frame name or library. For single-service deep dives in…

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Retrieves all pull requests for a repository that have a specific head branch. This is useful for finding active PRs associated with a particular feature branch or development branch.

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Retrieve specific rows from a reference table by their primary key values. Returns the row data in tab-separated format for easy readability. Use list_reference_tables first to discover table IDs.

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Get an AI-generated play-by-play of what a user did during a session replay — what pages they visited, what actions they took, and what happened step by step. Use this tool when the user wants to understand the content of a specific session: "what did the user do", "summarize this session", "what happened in this repl…

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Get a pre-computed performance insight for a specific RUM view. Call this to diagnose a performance problem on a known view. Choose insight_name based on what you want to investigate: 'aggregated_waterfall' to find slow or blocking network requests (use when loading_time, LCP, or FCP is degraded); 'aggregated_long_tas…

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Investigate why a RUM operation is failing or slow. Accepts either operation_id or operation_name (at least one is required). You MUST call search_rum_operations first to confirm the operation exists (and to get its operation_id/application_id) — if it doesn't show up there, stop and do not call this tool, since a typ…

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Get a deep-dive health summary for a single RUM operation, identified by operation_id (preferred — skips name resolution) or operation_name (at least one is required). SDK-instrumented operation names come from application code and can repeat across RUM applications; pass application_id to disambiguate which one to su…

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Returns a performance summary for a RUM application with period-over-period diffs and anomaly windows. Covers browser metrics (LCP, FCP, CLS, INP, loading time, time spent) and mobile metrics (refresh rate, memory, slow frames, frozen frames, CPU ticks, ITNV, network settled, TTID, TTFD, crash-free rate, ANR rate, han…

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Reconstruct the chronological load timeline for ONE specific RUM view occurrence, web or mobile. Returns every resource, long task, error, and user interaction (action) that occurred during that single view, ordered by start time, each carrying the internal `resource_id`/`long_task_id`/`action_id`/`error_id` needed to…

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Get a saved filter by id, including its targeting rules.

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Retrieves detailed health metrics for a single Spark or Databricks job run. This tool extracts structured health data from Spark/Databricks job traces including: - Target metrics: duration, executor CPU time, allocated executor time, max executors - Supplementary metrics: peak memory, shuffle reads, spilled bytes, dis…

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Retrieves the Spark SQL physical execution plan from a spark.stage span. This tool extracts and formats the _dd.spark.sql_plan attribute from a Spark stage span. The execution plan shows: - Node types (Exchange, SortMergeJoin, HashAggregate, etc.) - Join strategies and keys - Shuffle/partitioning information - Child n…

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Return all Kafka Schema Registry versions for a single subject on a given Kafka cluster. Use this after list_all_kafka_schemas to inspect the full version history (schema body, id, version, references) of a specific subject.

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Search and retrieve details about Synthetics tests configured in Datadog. Use this tool to either query test configurations, or test results. Supports filtering by endpoint domain and/or path, or public ID for test configurations. A test configuration contains all the details about a test, including the endpoint domai…

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do-not-call

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Fetch recent queries that touched one or more specific entities, in reverse chronological order (most recent first). Use this when the user wants to understand query activity for a specific table or entity: - Who has been querying this table recently? - What SQL is being run against this entity? - What writes have hap…

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Get TypeScript schemas and building instructions for widget types. Call before generating widget JSON. Note: "group" is a container for organizing related widgets — include both "group" and any intended child widget type/s in one call for deduplication.

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Inspect Data Quality monitor data, anomaly bounds, and existing annotations before proposing a change. Monitor IDs are stable request identifiers, but when a monitor name has already been resolved, use the name as the primary user-facing label and put the ID in parentheses. Never mention group_hash when it is "0"; tha…

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Step-by-step instructions for adding initial Datadog Observability setup to Kubernetes applications and configurations. You must first review the user's project and this tool's arguments (including any nested arguments) and fill out as many of them as possible before calling this tool. Check project files and dependen…

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Installs and configures the Datadog Agent on a Linux host to collect infrastructure metrics, APM traces, and logs.

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List every Kafka Schema Registry schema known to Data Streams Monitoring (DSM) for the current org within a time window. Returns subject, cluster, version, schema type, and compatibility metadata. Use this to discover what subjects exist before drilling into their version history with get_subject_kafka_schemas.

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List allocations for a specific feature flag. *** FEATURE FLAG DETECTION *** If users mention: flags, toggles, feature switches, A/B tests, experiments, gradual rollouts, canary releases, or say they want to 'flag' something, this is feature flag work and should use feature flag tools. This is a secondary tool - use a…

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List Datadog Audit Trail events over a time window. Audit Trail monitors user activity across the Datadog platform to help maintain compliance, enforce platform governance, and build transparency, capturing audit events across configuration, access, and billing assets (monitor edits, API key creation, role changes, da…

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Get an overview of autonomous system (AS) health from Network Path data, without needing a specific ASN. Calls the Network Path aggregate endpoint with no ASN filter and returns a compact summary of which autonomous systems are currently degraded. **Input**: - from / to: optional time window as Unix timestamps in mill…

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List a database's ready-to-apply, benchmark-validated query optimizations — the same set shown on the Datadog DBM Optimizations page. Each entry carries a MEASURED improvement, so results can be ranked by impact. THIS IS THE RIGHT TOOL whenever the user asks about optimizing queries: "what query optimizations can I ma…

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List automation rules for security findings. Returns the ordered list of rules for a given rule type (first-match-wins evaluation order). Rule types: mute (suppress findings), due_date (set remediation deadlines), ticket_creation (auto-create Jira/Case Management tickets), severity_modifier (adjust finding severity).…

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List available Datadog skill guides. Skills document how to use Datadog's tools: the right attributes, query syntax, and common pitfalls. Call this before load_datadog_skill whenever you are not already certain of the exact skill name; skill names are not predictable from topic words. Use query for fuzzy search across…

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