datadog_metrics_catalog_list

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

With no annotations, the description carries the full burden. It states the tool is a listing operation (no destructive hints), references the API endpoint, and specifies output format (YAML). It does not mention pagination or rate limits, but for a simple catalog listing this is adequate. The mirror note adds helpful context.

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?

Three sentences: first states purpose/endpoint, second distinguishes from sibling and explains parameters, third clarifies output format and CLI mirror. Every sentence adds value; no fluff.

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 simple parameter set (2 optional), no output schema, and no nested objects, the description covers purpose, parameters, output format, and sibling distinction fully. No gaps remain for an AI agent to use this tool correctly.

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 coverage is 100% with detailed property descriptions already present. The description restates the parameters' roles ('since `from`', 'optionally filtered by `host`') but adds no new meaning beyond what the schema already provides. Baseline 3 is appropriate.

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 via `/api/v1/metrics`, returns metric names, and explicitly distinguishes itself from `datadog_metrics_query` which returns data. This provides a specific verb+resource and distinguishes from siblings.

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 explains when to use this tool (to list metric names) vs the alternative (`datadog_metrics_query` for data). It also mentions optional filtering by `from` and `host`. No explicit when-not or other exclusions, but the distinction is clear enough for an AI agent.

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