Datadog MCP

Read dataset records with structured previews + a schema summary. Records' `input` / `expected_output` / `metadata` are arbitrary JSON — this tool shapes them so the model sees structured previews (objects keep keys, lists keep length+head sample, strings truncate cleanly) instead of broken char-truncated text. **Use…

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Get aggregate statistics for a specific evaluator over a time window, optionally filtered by ML application. **Use cases:** - Check the overall pass rate for an evaluator - Get score distribution (mean/p50/p90) for a score evaluator - Inspect top categorical values for a categorical evaluator - Check true/false distri…

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Retrieve an LLM-judge evaluator configuration by name. Returns the full config including target (ml_app + sampling + filter), LLM provider, and judge prompt template. Returns a not-found error if no evaluator with this name exists for the caller's org.

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Get the unique values for a dimension, with counts. Use this to discover valid filter values before calling **list_llmobs_experiment_events** or segment values before calling **get_llmobs_experiment_metric_values**. Returns dimension, unique_count, and values[] (each with value and count).

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Get full details for a single experiment event, including its input, output, expected_output, all metrics, and dimensions. Use this after **list_llmobs_experiment_events** to inspect a specific event in depth.

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Get statistical analysis for a specific evaluation metric, optionally segmented by a dimension. Returns aggregate statistics, not per-event raw values. **Use cases:** - Compare a metric (e.g., accuracy) across dimension segments (e.g., prompt versions) - Get percentile distributions (p50/p90/p95) for score metrics - C…

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Get a high-level summary of an experiment with pre-computed statistics for all evaluation metrics. Start here before using other experiment tools. Returns pre-computed stats (score/boolean/categorical) grouped by eval type, plus available dimensions for filtering. Use **list_llmobs_experiment_events** to browse indivi…

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Fetch up to 3 specific records with **untrimmed** input / expected_output / metadata. Resolve `record_ids` via **get_llmobs_dataset_records** first. Returns `DatasetRecordsFullResult` with full record bodies. On invalid input or API failure returns a `DatasetRecordToolError`. The cap of 3 is intentional: full bodies c…

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Get canonical LLM Observability model pricing rate cards. Provide at least one of provider or model. `provider` is an optional hint: when it matches a known provider the search is scoped to it; otherwise (empty or unknown provider) a model query searches the whole pricing catalog so you still get candidates to reason…

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Get the most-recently-modified **Topics Discovery (Patterns)** configuration for the caller's org. Returns a not-found error if the org has no config yet. Use **list_llmobs_pattern_configs** instead when you need to see all configs or resolve a specific `config_id`.

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Get a **cursor-paginated page of clustering points** (individual spans) assigned to a single topic. Each point includes the `span_id`, `session_id`, and a span input preview. Resolve `topic_id` from **get_llmobs_patterns** or **get_llmobs_patterns_with_points**. Pass the returned `next_page_token` back in as `page_tok…

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Get the status and per-activity progress of the **most recent** Topics Discovery run for a config. Use this to tell whether clustering is still running, has completed, or failed before reading topics. Returns the run `id`, `status`, current `step`, and a `progress` list of activities. Resolve `config_id` via **list_ll…

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Get the **topic hierarchy** discovered by a Topics Discovery run. Topics are organized into levels; each topic has a `name`, `description`, and `point_count`. Provide `config_id` (required). Omit `run_id` to read the most recent completed run, or pass a specific run from **list_llmobs_pattern_runs**. To also get the s…

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Get the topic hierarchy for a run **with the clustering point span IDs inlined** on each leaf (hierarchy-0) topic. Use this when you want both the topics and the spans that back them without a second call per topic. Set `include_metrics=true` to also inline per-span duration, cost, token counts, and evaluations (heavi…

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Look up an LLM Observability **experiments project** by ID or name. Pass `project_id` (UUID) for a direct lookup or `project_name` to resolve by name. If you only have an `ml_app`, prefer **search_llmobs_spans** to find spans for the ml_app — projects are an experiments-only concept and not the same as ml_apps. Return…

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Retrieve the actual content of a specific field from a span. Call this after **get_llmobs_span_details** reveals which content fields are available via content_info. Content fields contain the span's I/O, LLM conversation messages, retrieved documents (RAG), or user-attached metadata. Use the **path** parameter (JSONP…

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Get detailed metadata for one or more spans within a trace. Returns everything except the actual content payloads — use **get_llmobs_span_content** to retrieve those. Each span includes identification, timing, error info, LLM details (model, token counts), metrics, evaluations, and a content_info map. The **content_in…

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Get the full structure of a trace as a span hierarchy tree. Use this after **search_llmobs_spans** to understand the shape of a trace before drilling into specific spans. Returns trace overview (span counts by kind, error indicators, total duration) and a nested span tree when include_tree=true. Each node includes spa…

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Finds monitoring gaps and coverage for services or hosts. Returns which signals (error rate, latency, request rate) are covered by monitors and which are missing. Use with create_datadog_monitor to fill gaps. Query examples: 'service:my-service', 'host:my-host', 'service:*' (default).

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Retrieves official Datadog monitor templates as starting points for creating monitors. Use with create_datadog_monitor.

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Get detailed information about a network device by its device_id. Use this to investigate device health and connectivity (ping status), get hardware details (vendor, model, serial number), check OS info for patching/compatibility, or view device location and tags. Workflow: Use search_ndm_devices first to get the devi…

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Retrieve the full text of an NDM network device configuration snapshot (running config or startup config) for review, compliance auditing, or troubleshooting. Use a config_id from search_network_device_configurations results. Returns the complete config_content along with metadata (config_type, config_source, created_…

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Search and retrieve Network Path test runs using a query. Network Path tests data contains traceroute runs and hop-by-hop data. **Query Syntax**: Use search syntax to filter test runs. Common query patterns include: - Search by test ID: 'test_id:abc123' - Search by source/destination: 'source.hostname:"my-server"' or…

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Use this FIRST — before create-feature-flag — whenever a user asks to set up, add, or onboard Datadog feature flags in a project that doesn't have them yet: e.g. "set up feature flags", "add feature flags to my app", "get started with feature flags", "onboard this app onto feature flags", "start using Datadog feature…

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Rank database tables by query activity, broken out by who is querying them. Returns one signal per user type, each an independently ranked list of the top N tables for that user type sorted by query count: human — real users (engineers, analysts). Strongest signal for business-critical tables. bi_tool — BI tools (Meta…

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Discover available fields and facets that can be used for filtering and grouping profiling data. Use this to find what groupBy or filter fields are available for get_profiling_timeseries queries. Returned fields only applicable to get_profiling_timeseries queries. Returns an object with a "fields" array. Each element…

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Get the values for a specific profiling field/facet discovered via get_profiling_fields. Use this to discover what values a particular field has (e.g. all endpoint values, all function names). Returned field values only applicable to get_profiling_timeseries queries. For tag values (service, host, env, etc.) use get_p…

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Returns profile types and families that can be used by other profiling tools given a query context. You can either query by tags (queryString + timeFrame) or by trace context. When using trace context, the tool will find profiles associated with that specific trace and span.

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Returns individual profiled runtime IDs (processes/containers). These are exact strings that can't be changed. For family, don't use ebpf.

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Returns insights over the provided time window. These insights contain: - A high-level summary explaining the issue and why it matters - Contextual insights from profiling data (for example, affected methods, packages, or processes) - Recommended next steps to help you resolve the issue * Use get_profiling_tag_names/g…

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