datadog_metrics_catalog_list

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

No annotations are provided, so the description carries the full transparency burden. It declares the tool is read-only, specifies the output format (YAML), and notes it mirrors a specific command. This adds behavioral context beyond the schema, though it doesn't mention potential pagination or rate limits.

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

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

The description is compact, using only three sentences to convey purpose, usage, filtering, read-only nature, output format, and sibling distinction. No extraneous information; every sentence serves a clear function.

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

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

Given the tool's simplicity (2 optional params, no output schema, no nested objects), the description fully covers what an agent needs: what it returns (metric names), the output format (YAML), the filtering options, and how it relates to a sibling tool. No gaps are apparent.

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

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

Schema description coverage is 100% for both parameters (from and host), but the description adds extra value by providing concrete examples (e.g., epoch timestamp 1700000000, host web-01) and clarifying semantics like 'ingested since this timestamp' and 'omit for all hosts.' This reinforces and expands the schema descriptions.

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

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

The description clearly states the tool lists metrics in the Datadog catalog, specifying the API endpoint (/api/v1/metrics). It explicitly distinguishes from the sibling tool datadog_metrics_query by noting it returns metric names (e.g., system.cpu.user) rather than timeseries, allowing an agent to differentiate usage.

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

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

The description provides explicit guidance: 'use this to discover what to query, then datadog_metrics_query to fetch the actual timeseries.' It explains the optional parameters (from, host) and clarifies that omitting host returns all hosts. This tells the agent exactly when to choose this tool over alternatives.

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