Developer Tools

Datadog

by Datadog

Overview

Analyze, investigate, and act on your Datadog telemetry directly from Claude using natural language. Ask questions about your production applications. Identify, visualize, and remediate issues in your critical services. Run agentic loops to ensure you continue to maintain good observability and service management posture.

Tools

_dd_gff

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_dd_guc

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_dd_wfd

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aggregate_events

ChatGPT

Aggregate Datadog events to compute counts, sums, averages, min, max, cardinality, and percentiles (P50, P75, P90, P95, P99), with optional grouping by fields or time intervals. Use this for aggregated analysis such as event counts by source, event frequency over time, or grouped summaries across tags. For raw event inspection, titles/messages, or tag exploration, use search_datadog_events.

aggregate_rum_events

ChatGPT

Aggregate Datadog RUM events to compute counts, sums, averages, min, max, cardinality, and percentiles (P50, P75, P90, P95, P99), with optional grouping by fields or time intervals. Use this for aggregated analysis of RUM data such as session counts over time, error counts by page, or p95 loading times by browser or URL path. For raw event inspection or attribute discovery, use search_datadog_rum_events.

aggregate_spans

ChatGPT

Aggregate Datadog APM spans to compute counts, sums, averages, min, max, cardinality, and percentiles (P50, P75, P90, P95, P99), with optional grouping by fields or time intervals. Use this for aggregated analysis such as request counts over time, p95 duration by service or resource, or error counts grouped by endpoint or status code. For raw span inspection or to discover fields to group by or aggregate on, first use search_datadog_spans and optionally request custom_attributes.

analyze_datadog_logs

ChatGPT

Analyze Datadog logs using SQL. Runs against a virtual 'logs' table filtered by your search query. Good for aggregations, counts, group-bys, or peeking at recent logs with LIMIT. To discover custom attributes for extra_columns, first call search_datadog_logs with extra_fields. If a query times out, try a shorter time range.

ask_widget_expert

ChatGPT

Get targeted instructions for building a Datadog widget. Returns a concise how-to guide — widget type recommendations, required fields, schema patterns, and an annotated example — that you use to build the widget definition yourself. The expert has deep knowledge of all widget types and schemas but does NOT have access to real telemetry, live data, or the user's environment. Any examples in the response use PLACEHOLDER values for queries. You are responsible for discovering real metric names, log queries, or APM resource names through data exploration tools and substituting them — do not copy example queries directly into a widget without first validating them against real data. Returns focused instructions rather than a full schema dump, so it's cheaper than get_widget_reference. Useful at any stage: choosing a widget type, understanding schema requirements, or debugging an invalid widget.

create_annotation

ChatGPT

Create an annotation on a Datadog dashboard or notebook. Annotations mark a moment or range on a widget's graph to provide context about events (e.g. deployments, incidents, config changes). Omit end_time for a point-in-time annotation; provide end_time for a time-range annotation.

create_datadog_notebook

ChatGPT

Creates a new Datadog notebook. Include key findings, evidence, supporting data, and complete query documentation unless told otherwise. Do not start the first markdown cell with a heading that repeats the notebook name — it is already displayed as the title. Look up widget reference schemas before constructing graph cells.

delete_annotation

ChatGPT

Delete a user-authored annotation by its ID. The caller must have edit access to the dashboard or notebook the annotation belongs to.

edit_datadog_notebook

ChatGPT

Edits an existing notebook. Can edit cells, metadata (name, template_variables, tags), or both. Look up widget reference schemas before constructing graph cells. Cell modes: (1) Full-replace (default) — replaces all cells; destroys existing comment marks. (2) Append (append_only=true) — adds cells to the end; preserves comment marks. To edit only metadata, omit the cells parameter and provide at least one of name/template_variables/tags. Metadata edits are done on a best effort basis and can be combined with either cell mode.

execute_code

ChatGPT

Execute JavaScript in a sandbox with access to Datadog through the official TypeScript-SDK-shaped API and explicitly enabled code-exec extensions. Quick start: ``

javascript import { client, v1 } from "@datadog/datadog-api-client"; const monitors = new v1.MonitorsApi(client.createConfiguration()); return await monitors.listMonitors({ page: 0, pageSize: 1 });

` The dd global only exposes dd.time.`. Product APIs are not mounted under `dd`; import official APIs from `@datadog/datadog-api-client` and construct the appropriate `v1` or `v2` API class as shown above. Reviewed APIs that are not in the official SDK are exposed separately through the sandbox-only `@datadog/code-exec` package when enabled for the current session. Datadog APIs are available through both the official TypeScript SDK and enabled code-exec extensions. Code-exec's value is that you can compose API calls in one script, join data across products, and return a compact result — not that you need a separate MCP helper for each Datadog query. SDK discovery requirement: Do not rely on model memory for Datadog SDK method names, request fields, enum values, or response shapes. Before calling `execute_code`, call the separate `search_datadog_sdk` MCP tool unless the exact operation and complete request shape are already present in a known-good example below or were returned by an earlier SDK search in this conversation. If `execute_code` returns `invalid_request`, an unknown method or member error, or a request-shape error, call `search_datadog_sdk` before retrying. Do not repair these errors by guessing fields. Its metadata-only catalog only returns official SDK methods and code-exec extensions available in the current `execute_code` session. In one JavaScript program, select the operation and return its `usageNotes` plus a bounded closure of its `typeRefs` from `catalog.types`. For official SDK operations, use the declared version, API, method, and TypeScript types. For extensions, use the declared module, export, method, and TypeScript types. Do not guess fields from an HTTP path or summary. For non-trivial investigations where you need a multi-query script, API aggregation, cross-source joins, or compact ranked output, load the `datadog/code-exec` skill before writing the script. Skip it for trivial one-liners or when the exact request shape is already clear. Code-exec guidance lives in `datadog/code-exec` and its bundled references; don't guess product-specific skill names like `datadog/traces` or `datadog/metrics` from the task topic. Execution discipline - `dd.time.

 returns RFC3339 strings. Logs and spans accept those strings directly. Metrics scalar and timeseries requests require Unix milliseconds; use

Date.parse(dd.time.hoursAgo(...)) and Date.parse(dd.time.now()), and do not hand-calculate Unix timestamps. - After an invalid_request

, re-check the operation signature, type declarations, and usage notes before retrying. Verify timestamp units, enum values, and exact camelCase property names. - Compose related queries in one script and return a compact result. Use

Promise.all when every result is required. When independent calls can still provide useful evidence if a sibling fails, use Promise.allSettled

 and return the successful evidence alongside a compact failures array. When a script returns after mixed API-call outcomes,

execute_code reports outcome: "partial" and partial: true; the returned value remains usable, and diagnostics.api_calls

 describes the failed calls. Treat partial evidence as incomplete and retry only the missing source when it is necessary for the answer. If every API call fails, the execution is still reported as a failure even if the script caught the rejections. Investigation rules - Use aggregate APIs for totals, top-Ns, rankings, ratios, and percentiles. Prefer aggregate operations where supported. Do not group returned samples and present them as global rankings. - For top-N aggregations, put the sort on the

groupBy entry. A limit without sort` does not guarantee the first bucket is the…

get_active_feature_flags

ChatGPT

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get_change_stories

ChatGPT

Retrieve change events for an APM service over a time range. Use to correlate changes with performance issues, errors or incidents. Tracked Change types: deployment (code/version), feature_flag, traffic_anomaly, watchdog (Datadog anomaly detection), kubernetes (k8s deployment manifest updates), scale (manual k8s scale events), crashloopbackoff (k8s crashloops), database (DB schema or setting changes), schema (data stream schemas only; not DB/API schema), configuration (limited to specific tracked sources — absence does not imply no config changed)

get_datadog_dashboard

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Retrieves a Datadog dashboard by ID, returning its title, description, tags, and widgets. Use search_datadog_dashboards first to find dashboard IDs.

get_datadog_incident

ChatGPT

Get detailed information about a specific Datadog incident by ID, including status, severity, timeline, and associated users.

get_datadog_metric

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Query metrics data from Datadog. Use response_format='timeseries' (default) to get time-indexed data points for graphs and trend analysis. Use response_format='scalar' to get a single aggregated value per group, useful for current state, summaries, and comparisons. For response_format='scalar', use structured query objects; the aggregator field controls how the time window collapses to a single value and defaults to avg if omitted. The query prefix, such as avg: or sum:, is not the scalar aggregator field. Supports multiple queries in one call plus formula expressions. Query syntax: grouping must come before modifiers, e.g. 'sum:trace.http.request.errors{env:prod} by {service}.as_count()', not '.as_count() by {service}'. Within {...}, do not mix comma-separated tag filters with boolean filters; if any top-level filter uses AND, OR, NOT, or IN, join top-level filters with explicit AND and parenthesize OR groups, e.g. '{(service:api OR service:worker) AND datacenter:us1.prod.dog}', not '{service:api OR service:worker,datacenter:us1.prod.dog}'.

get_datadog_metric_context

ChatGPT

Get metadata (description, type, unit, integration), available tags/dimensions, and optionally related assets for a metric. Useful for exploring metrics before querying them. Set use_cloud_cost=true for Cloud Cost Management metrics.

get_datadog_notebook

ChatGPT

Retrieve information about a specific Datadog notebook by ID. This tool provides details including name, status, and associated author. The ID can also be extracted from a URL. The ID will be the last component, for example, <host>/notebook/<ID>.

get_datadog_trace

ChatGPT

Retrieve a trace by trace ID from Datadog APM. This tool fetches all spans within a specific trace by default, providing detailed information about the request flow, timing, and service interactions. For large traces or to retrieve a summarized trace, set only_service_entry_spans=true to get a hierarchical condensed view that shows service boundaries, collapsing internal operations. The summarized view will indicate expandable spans with hidden_child_spans_count. The response includes a trace_deep_link_url in metadata that links directly to the trace flamegraph in the Datadog UI.

get_user_config

ChatGPT

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get_widget

ChatGPT

Retrieve and visualize Datadog metrics, traces, logs, and other data as interactive charts. Use this tool whenever users ask to see, show, graph, plot, visualize, or rank Datadog data — including ranking-style queries like 'which hosts/services/pods have the most/least X', 'top N by Y', or 'breakdown by Z'. Covers metrics timeseries, request counts, latency, error rates, toplists, and distributions. This tool renders the chart directly and does not require pre-fetched data — raw-data tools alone aren't sufficient for visualization requests. Input modes: Direct definition (write a new widget via widget_definition), By ID (existing widget via dashboard_id + widget_id), or By URL (Datadog widget_url). Supported interactive types: bar_chart, change, distribution, heatmap, list_stream, query_table, query_value, scatterplot, sunburst, timeseries, toplist, treemap. Before constructing widget_definition, load the datadog/visualizations skill for schema patterns, response shape, and recovery.

get_widget_data_fetcher

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get_widget_reference

ChatGPT

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.

get_widget_reference_compressed

ChatGPT

Get compressed 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.

list_annotations

ChatGPT

List user-authored annotations on a Datadog dashboard or notebook for a given time range. Returns annotations ordered by start_time.

list_datadog_skills

ChatGPT

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 name and description (results are ranked). Use include_header=true to see one-line summaries alongside names. Load skills proactively when starting work in a relevant Datadog domain, not after errors.

list_mcp_feedback

ChatGPT

List feedback submitted about Datadog MCP server capabilities. This only includes feedback for this Datadog MCP server, not other MCP servers or plugins. You must specify either org_id or all_orgs (but not both).

load_datadog_skill

ChatGPT

Load a Datadog skill guide before using related tools (for example, load 'datadog/traces' before searching spans, or 'datadog/logs' before searching logs). Skills improve query quality by documenting the right attributes, syntax, and pitfalls. If you do not already know the exact skill name from a prior list_datadog_skills response, call list_datadog_skills first; do not guess names from topic keywords. Set header_only=true to preview a skill's summary, related skills, and bundled resources. Set resource_path to load a specific bundled reference.

search_datadog_dashboards

ChatGPT

List and retrieve information about Datadog dashboards. This tool helps discover available dashboards, their IDs, titles, and underlying queries. Use this tool when you need to find specific dashboards, get an overview of all dashboards in your Datadog account, or find important logs+metrics queries in dashboards.

search_datadog_entities

ChatGPT

Search Datadog entities through the entity graph, including attributes and direct relationships. Prefer this tool over searching telemetry, monitors, dashboards, or other resources when the request is about service identity, catalog ownership, dependencies, or services owned by the current user's teams. Use entity_type=service directly for identity lookups and dependency questions fully covered by available guidance; kind: is added by the server. Search directly only when exact names are established by available guidance. Do not use validation errors to discover fields or relationships, and do not probe plausible names. Ref, type, and known type-specific identity fields are always returned, so omit fields for name-only discovery. Treat dependency requests and explicit requests for all, every, complete lists, or inventories as complete. Explicit all, every, complete-list, and inventory requests override this bounded-search rule. Treat owner- or team-filtered service searches as complete unless the user explicitly requests a sample, maximum count, or first page. For complete results, continue while METADATA.page_truncated is true, page_next_cursor remains, or a requested relationship is truncated or has next_cursor. Do not infer completion from a short page. Set owned_by_current_user_teams=true instead of inferring the user's teams from telemetry. For service ownership, request the owner field; do not invent an ownership relationship. Query supports boolean expressions such as name:chrono OR owner:idp. Use exact name:value when the user supplies a concrete internal service name; do not broaden it with wildcards. Use name:value for prefix requests. For vague possible-match requests that do not require complete results, make one search with page.limit at most 25 using name:value*. Present an incomplete, non-ranked candidate sample and ask the user to narrow the request; do not paginate. For a dependency question with no direction, request both upstream_services and downstream_services and keep the directions separate. Filters and relationship names are validated by the entity graph. Requested fields may be omitted, so do not treat an absent field as empty. Use owned_by_current_user_teams only when guidance for the selected entity type says it is supported.

search_datadog_events

ChatGPT

Search and retrieve raw Datadog events (deployments, alerts, system activities, infrastructure changes, etc.). Do NOT use for counts, aggregations, or grouped analysis — use aggregate_events instead. Best for: inspecting individual events, reading event titles/messages/tags, and exploring event patterns before deciding how to aggregate them. Supports complex queries with boolean operators and tag filtering. Results include event titles, messages, timestamps, and tags.

search_datadog_hosts

ChatGPT

Explore Datadog hosts inventory with SQL. Queries run against the virtual 'hosts' table backed by "dd.hosts" (Advanced Query API), which exposes hostname, hostname_aliases (text[]; use hostname_aliases[1]), tags (hstore map; use tags->'key'), cloud_provider, resource_type, instance_type, os, os_version, agent_version, memory_mib, cpu (hstore map; use cpu->'key'), kernel (hstore map; use kernel->'key'), sources (text[]; use sources[1]), modification_detected_at (timestamp). DDSQL notes: DDSQL (Datadog SQL) is a PostgreSQL subset: every non-aggregated SELECT column must appear in GROUP BY, SELECT aliases cannot be reused in WHERE/GROUP BY/HAVING (repeat the full expression instead, e.g., GROUP BY DATE_TRUNC('hour', timestamp) not GROUP BY hour), and only declared table columns may be referenced. Use the -> operator or json_extract_path_text (cast as needed) for JSON access. Avoid unsupported constructs like ANY(), ->>, QUALIFY, information_schema, and current_timestamp. Column names containing special characters like '@' must be quoted (e.g., SELECT "@foo" FROM logs).

search_datadog_incidents

ChatGPT

Search Datadog incidents. Default sort: newest first. Supports filtering by state, severity, title, team, creation time, and more. Use semantic_query for natural-language search over AI-generated incident summaries. Prefer query first; only add semantic_query when query by keyword and metadata filters aren't sufficient to answer the question.

search_datadog_logs

ChatGPT

Search and retrieve raw log entries or log patterns. Do NOT use for counting or aggregations — use analyze_datadog_logs instead. Best for: viewing raw logs, discovering patterns (use_log_patterns=true), and discovering custom attributes via extra_fields for use as extra_columns in analyze_datadog_logs.

search_datadog_metrics

ChatGPT

List available metrics in Datadog with optional filtering by name and tags. Set use_cloud_cost=true to search Cloud Cost Management metrics.

search_datadog_monitors

ChatGPT

List and retrieve information about Datadog monitors. This tool helps discover monitors, their status, configuration, and alerts. Use this tool when you need to find monitors for investigation, management, or analysis purposes.

search_datadog_notebooks

ChatGPT

Search Datadog notebooks. Use this tool when you need to find notebooks for investigation or monitoring purposes. Results can be filtered, for example by author, and sorted. This tool returns snippets of the original notebook. To fetch the entire notebook, use the get_datadog_notebook tool.

search_datadog_rum_events

ChatGPT

Search and retrieve raw Datadog RUM events using advanced query syntax. Do NOT use for counts, aggregations, or grouped analysis. Best for: inspecting individual RUM events, debugging specific user experience issues, exploring available event attributes in raw or detailed_output responses before deciding what to aggregate. Supports filtering by event types (session, view, action, error, resource, long_task, vital, operation), user attributes, performance metrics, and more. Results include detailed event data for analysis.

search_datadog_sdk

ChatGPT

Discover the exact operations and types needed for execute_code. Write JavaScript against the official SDK and code-exec extension catalog available to the current user and sandbox policy. Its data stays inside the sandbox; only the value you return enters the conversation. This tool reads metadata, not live Datadog data. Select the operation and collect its request/response types in the same script. Start from the operation's typeRefs, then follow each type's refs to expand nested definitions. Returning only the immediate types often leaves request fields undefined and requires another search. Follow those references rather than guessing related type names. Quick start: select an operation and expand its referenced types before returning. The helper below is ordinary JavaScript defined in this example, not a built-in catalog method. Adapt the predicate to the task; you don't need to know nested type IDs in advance. ``

javascript const operation = catalog.operations.find( (op) => op.version === "v2" && op.api === "MetricsApi" && op.method === "queryScalarData" ); if (!operation) return { operation: null }; function collectTypes(rootIds, maxTypes = 24, maxChars = 12000) { const definitions = []; const omittedTypeIds = []; const pending = [...rootIds]; const seen = new Set(); let chars = 0; while (pending.length > 0) { const id = pending.shift(); if (seen.has(id)) continue; seen.add(id); const definition = catalog.types[id]; if (!definition) continue; if ( definitions.length >= maxTypes || chars + definition.declaration.length > maxChars ) { omittedTypeIds.push(id); continue; } definitions.push(definition); chars += definition.declaration.length; pending.push(...definition.refs); } return { definitions, omittedTypeIds }; } return { operation, types: collectTypes(operation.typeRefs), };

` Return the selected operation, including its signature and usageNotes

, together with bounded type definitions and any omitted type IDs. If expansion omits a definition needed to construct the request or interpret the response, retrieve that definition before executing. Otherwise, proceed to

execute_code

; a separate type-lookup call isn't required. For broad matches, return a small operation shortlist. For an operation you intend to execute, include its referenced types. Narrow the operation selection before reducing the type-expansion budget. Don't return the complete operations or types collections. There is no

search(...) helper. catalog.operations is an array; catalog.types is a record keyed by type ID, not an array. The catalog is function-free: there is no built-in describe, continuation callback, or executable next

 value. Use normal array/object traversal. Your code runs as the body of an async function. Write plain JavaScript, not TypeScript, and use

return to return JSON. There is no dd

, package loader, network, or Datadog API access in this metadata-only sandbox. Catalog contract Filter operations by product API (for example,

LogsApi, SpansApi, or MetricsApi), method, summary, tags, or HTTP path. Use .filter(...).slice(0, 3)

 for a bounded shortlist; when selecting several operations to execute, collect their referenced types in the same program. Catalog operations have two invocation surfaces. Official SDK operations use

version, api, and method through @datadog/datadog-api-client. Extension operations use surface: "code-exec" plus their exact module, export, and method through the sandbox-only @datadog/code-exec package. Don't translate either surface to dd.<namespace>; product API/helper namespaces aren't exposed on dd. For example, an operation with version: "v2", api: "SpansApi", and method: "aggregateSpans" is called as: `

javascript import { client, v2 } from "@datadog/datadog-api-client"; const spans = new v2.SpansApi(client.createConfiguration()); const request = { /* fields from the discovered request declarations */ }; const result = await spans.aggregateSpans(request);

` `typescript type SDKVersion = "v1" | "v2"; type TypeID = ${SDKVersion | "cod…

search_datadog_service_dependencies

ChatGPT

Retrieve information about Datadog service dependencies. This tool helps discover upstream and downstream service dependencies in your environment as well as services owned by a given team. Use this tool when you need to find services for investigation, management, or analysis purposes. Either service or team must be supplied but not both; service for service dependencies, team for service ownership. If the capability exists, the service dependencies can be displayed visually as a graph, for example, as a Mermaid diagram. If outputting to them to a notebook, the mermaid markdown type can be used. If making any assumptions about service dependencies, ensure they are explicitly stated to the user.

search_datadog_services

ChatGPT

List and retrieve information about Datadog services. This tool helps discover services in your environment, their descriptions, teams, and links. Use this tool when you need to find services for investigation, management, or analysis purposes.

search_datadog_spans

ChatGPT

Retrieve raw Datadog APM spans matching a query. Do NOT use for counts, aggregations, or grouped analysis — use aggregate_spans instead. Best for: inspecting individual spans, debugging request flows and failures, and discovering span fields/attributes, including via custom_attributes, that you may want to aggregate or group by with aggregate_spans. IMPORTANT: When the user asks for 'security traces', 'AppSec traces', 'AAP traces', 'security activity', or 'security signals on traces', the query MUST start from @appsec.security_activity:*. Do NOT substitute keyword-based guesses like service:*security*, resource_name:*auth*, @http.status_code:401, etc. — those do not identify security traces. Application & API Protection (AAP) spans carry these standard attributes (all strings): @appsec.security_activity (<category>.<type>, e.g. attack_attempt.sql_injection, business_logic.users.login.failure; multi-valued when multiple rules match); @appsec.category (top-level classification, e.g. attack_attempt, business_logic); @appsec.type (specific threat/event type, e.g. sql_injection, xss, users.login.failure); @appsec.rule_id (AAP rule that matched, e.g. crs-942-100; multi-valued); @appsec.blocked (true/false, whether AAP blocked the request). Use these attributes to narrow AppSec queries (e.g. @appsec.category:attack_attempt, @appsec.type:sql_injection, @appsec.blocked:true). The response includes a traces_explorer_url for the overall query. To link to an individual trace in the Datadog UI, use the pattern: <base_url>/apm/trace/<trace_id> (e.g. https://app.datadoghq.com/apm/trace/abc123). The base_url for the user's org is available in the response metadata.

submit_mcp_feedback

ChatGPT

Submit feedback about capabilities provided by this Datadog MCP server only. Do NOT submit feedback about other MCP servers or plugins (e.g. Atlassian, CodeSandbox, Slack, GitHub). After completing a task, briefly consider whether any Datadog MCP capability was notably unhelpful or whether the workflow was harder than it should have been. If so, submit feedback so the MCP team can improve the experience. You should submit feedback when: - A Datadog MCP capability produced wrong, misleading, or unhelpful results that impacted your ability to help the user. - A Datadog MCP capability's output was missing key information that would have been useful. - You could not accomplish something the user asked for because no Datadog MCP capability exists for it. - A Datadog MCP workflow required many steps or workarounds that could be streamlined. - A Datadog MCP capability's interface was confusing or its parameters were unclear. Keep it to one submission per distinct issue per conversation. Skip transient errors (timeouts, rate limits) that resolved on retry.

update_annotation

ChatGPT

Update an existing annotation on a Datadog dashboard or notebook. All writable fields are replaced with the provided values, including widget_ids — pass the full intended targeting every call (use list_annotations first if you need the existing widget_ids). Omit end_time to make the annotation point-in-time; provide end_time to make it a time-range annotation.

upsert_datadog_dashboard

ChatGPT

Creates or updates a Datadog ordered-grid dashboard. New dashboards are always ordered; widget updates are only supported on ordered dashboards. When updating widgets, prefer diff-style payloads to minimize tokens: send full definitions only for new or changed widgets, include {"id": N} for unchanged widgets to keep, and omit existing widgets only when deleting them.

validate_dashboard_widget

ChatGPT

Validate a widget definition against the dashboard schema. Call when generating a widget JSON.

visualize_tabular_data

ChatGPT

Render tabular data as an interactive visualization (sunburst, treemap, or toplist). WHEN TO USE: - After aggregating data from queries (GROUP BY, analytics, metrics) to visualize hierarchical relationships or rankings - For ad-hoc visualization of data you've already fetched and transformed - When you have grouped data and want to show proportional breakdowns or parent-child relationships ALTERNATIVES: - Use get_widget to directly render data from existing dashboard widgets - Use swap_widget_type to convert saved widget definitions between types

aap_onboarding

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aggregate_datadog_ci_pipeline_events

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aggregate_datadog_test_events

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aggregate_dora_deployments

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aggregate_events

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aggregate_rum_events

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aggregate_spans

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analyze_cloud_network_monitoring

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analyze_datadog_logs

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analyze_datadog_security_signals

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analyze_security_findings

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append_reference_table_rows

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ask_widget_expert

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browser_onboarding

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check-flag-implementation

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code_coverage_onboarding

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create-feature-flag

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create_datadog_monitor

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create_datadog_notebook

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create_datadog_security_detection_rule

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create_datadog_security_suppression

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create_reference_table

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cws_agent_events_schema

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ddsql_create_link

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ddsql_get_spec

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ddsql_read_saved_query

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ddsql_run_query

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ddsql_schema_get_table_columns

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ddsql_schema_search_tables

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ddsql_schema_search_unstructured_fields

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ddsql_search_saved_queries

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ddsql_upsert_saved_query

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delete_datadog_dashboard

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delete_datadog_security_detection_rules

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delete_datadog_security_suppression

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delete_llmobs_evaluator

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describe_datadog_k8s_resource

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devices_onboarding

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docker_onboarding

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ecs_onboarding

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edit_datadog_notebook

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edit_synthetics_tests

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execute_datadog_workflow

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expand_llmobs_spans

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explore_profiling_call_graph

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explore_profiling_flame_graph

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explore_profiling_timeline

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find_datadog_database_instances

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find_llmobs_error_spans

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generate_monitor_message

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get-canary-results

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get-feature-flag

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get-guardrail-metric

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get_datadog_code_coverage_branch_summary

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get_datadog_code_coverage_commit_summary

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get_datadog_dashboard

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get_datadog_database_calling_services

Claude

get_datadog_database_explain_plans

Claude

get_datadog_database_health_signals

Claude

get_datadog_database_query_performance

Claude

get_datadog_database_query_statement

Claude

get_datadog_database_recommendations

Claude

get_datadog_database_schemas

Claude

get_datadog_error_tracking_issue

Claude

get_datadog_flaky_tests

Claude

get_datadog_flaky_tests_management_policies

Claude

get_datadog_incident

Claude

get_datadog_k8s_manifest

Claude

get_datadog_metric

Claude

get_datadog_metric_context

Claude

get_datadog_notebook

Claude

get_datadog_security_detection_rule

Claude

get_datadog_security_detection_rules_schema

Claude

get_datadog_security_signal

Claude

get_datadog_security_signals_schema

Claude

get_datadog_security_suppressions

Claude

get_datadog_test_optimization_settings

Claude

get_datadog_trace

Claude

get_datadog_workflow

Claude

get_datadog_workflow_instance

Claude

get_kafka_client_configuration

Claude

get_kafka_schema_registry_subject_versions

Claude

get_llmobs_agent_loop

Claude

get_llmobs_eval_aggregate_stats

Claude

get_llmobs_evaluator

Claude

get_llmobs_experiment_dimension_values

Claude

get_llmobs_experiment_event

Claude

get_llmobs_experiment_metric_values

Claude

get_llmobs_experiment_summary

Claude

get_llmobs_span_content

Claude

get_llmobs_span_details

Claude

get_llmobs_trace

Claude

get_monitor_coverage

Claude

get_monitor_templates

Claude

get_ndm_device

Claude

get_network_path_test_runs

Claude

get_profiling_field_values

Claude

get_profiling_fields

Claude

get_profiling_profile_types

Claude

get_profiling_runtime_ids

Claude

get_profiling_service_insights

Claude

get_profiling_services

Claude

get_profiling_tag_names

Claude

get_profiling_tag_values

Claude

get_profiling_timeseries

Claude

get_prs_by_head_branch

Claude

get_reference_table_rows

Claude

get_synthetics_tests

Claude

get_widget

Claude

get_widget_reference

Claude

kubernetes_onboarding

Claude

linux_onboarding

Claude

list-allocations-for-feature-flag

Claude

list-environments

Claude

list-feature-flags

Claude

list-guardrail-metrics

Claude

list_datadog_security_detection_rules

Claude

list_datadog_skills

Claude

list_datadog_workflows

Claude

list_kafka_broker_configuration_versions

Claude

list_kafka_schema_registry_schemas

Claude

list_kafka_topic_configuration_versions

Claude

list_llmobs_evals

Claude

list_llmobs_evals_by_ml_app

Claude

list_llmobs_experiment_events

Claude

list_reference_table_rows

Claude

list_reference_tables

Claude

llm_observability_onboarding

Claude

load_datadog_skill

Claude

monitor_groups_search

Claude

optimize_datadog_database_query

Claude

read_kafka_messages

Claude

recommend_monitor_threshold

Claude

retry_datadog_ci_job

Claude

search_cws_agent_events

Claude

search_datadog_ci_pipeline_events

Claude

search_datadog_dashboards

Claude

search_datadog_database_plans

Claude

search_datadog_database_samples

Claude

search_datadog_error_tracking_issues

Claude

search_datadog_events

Claude

search_datadog_hosts

Claude

search_datadog_incidents

Claude

search_datadog_k8s_resources

Claude

search_datadog_logs

Claude

search_datadog_metrics

Claude

search_datadog_monitors

Claude

search_datadog_notebooks

Claude

search_datadog_rum_events

Claude

search_datadog_security_signals

Claude

search_datadog_service_dependencies

Claude

search_datadog_services

Claude

search_datadog_slos

Claude

search_datadog_spans

Claude

search_datadog_test_events

Claude

search_dora_deployments

Claude

search_llmobs_spans

Claude

search_ndm_devices

Claude

search_ndm_interfaces

Claude

search_pr_insights

Claude

search_security_findings

Claude

security_findings_schema

Claude

security_signals_schema

Claude

serverless_onboarding

Claude

source_map_uploads

Claude

studio_onboarding

Claude

submit_mcp_feedback

Claude

sync-allocations-for-feature-flag-environment

Claude

synthetics_test_wizard

Claude

test_optimization_onboarding

Claude

update-feature-flag-environment

Claude

update_datadog_error_tracking_issue

Claude

update_datadog_flaky_test_states

Claude

update_datadog_security_detection_rule

Claude

update_datadog_security_signals_triage

Claude

update_datadog_security_suppression

Claude

update_datadog_workflow_with_agent_trigger

Claude

upsert_datadog_dashboard

Claude

upsert_llmobs_evaluator

Claude

validate_dashboard_widget

Claude

validate_monitor_definition

Claude

visualize_tabular_data

Claude

Capabilities

InteractiveWrites

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231

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Platforms