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TANTIOPE

Datadog MCP Server

metrics

Query timeseries data with PromQL, search metrics by name, list active metrics, or fetch metadata. Supports APM and percentile queries.

Instructions

Query Datadog metrics. Actions:

  • query: Get timeseries data (requires from/to time range, PromQL query)

  • search: Find metrics by name (grep-like, NO time param needed)

  • list: Get recently active metrics (last 24h, optionally filter by tag)

  • metadata: Get metric details (unit, type, description)

APM METRICS (auto-generated from traces): Keyed by OPERATION name (e.g. express.request, pg.query), NOT service name. Filter by service using tags: {service:my-service}

PERCENTILES (p50/p75/p90/p95/p99) — use the ROOT metric (distribution type): p95:trace.express.request{service:my-service}

AVG/SUM/MIN/MAX — use the .duration SUFFIX (pre-aggregated gauge): avg:trace.express.request.duration{service:my-service}

Other trace metrics (gauges):

  • trace..hits - Request count

  • trace..errors - Error count

  • trace..apdex - Apdex score

To discover operation names for a service, use: traces tool with action "services"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
queryNoFor query: PromQL expression (e.g., "avg:system.cpu.user{*}"). For search: grep-like filter on metric names. For list: tag filter.
fromNoStart time (ONLY for query action). Formats: ISO 8601, relative (30s, 15m, 2h, 7d), precise (3d@11:45:23)
toNoEnd time (ONLY for query action). Same formats as "from".
metricNoMetric name (for metadata action)
tagNoFilter by tag
limitNoMaximum number of results (for search/list, default: 50)
pointLimitNoMaximum data points per timeseries (for query action). AI controls resolution vs token usage (default: 1000).
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and thoroughly discloses behavioral traits: time range requirements, search as grep-like, list for recent 24h, metadata returns details. It also explains APM metrics structure (operation names, tags, suffixes for percentiles/avg). This is comprehensive transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear sections (Actions, APM Metrics, Percentiles, etc.). It is front-loaded with the core action list. Every sentence provides essential information for a complex tool, earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (8 params, 4 actions, APM intricacies) and no output schema, the description is remarkably complete. It covers time formats, action-specific requirements, advanced metric types, and even links to sibling tool 'traces' for discovery.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, setting baseline at 3. The description adds meaning beyond schema by clarifying parameter semantics: e.g., 'query' parameter meaning depends on action, from/to are only for query, metric for metadata, default limits. This adds significant value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool queries Datadog metrics and lists four distinct actions (query, search, list, metadata) with specific use cases. It distinguishes from sibling tools (e.g., traces, logs) by focusing on metrics, providing purpose clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidelines for each action, including required parameters (e.g., from/to for query) and search behavior (grep-like, no time param). It also includes advanced usage for APM metrics and percentiles. However, it does not explicitly state when not to use this tool vs siblings, though it's largely implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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