list_metrics

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

With no annotations, the description carries the behavioral burden and does well: it discloses case-sensitivity, the exact fields returned, and the fact that unfiltered output may be large. It does not mention pagination or rate limits, but those are minor for a metadata-listing tool.

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 two sentences with the main purpose front-loaded and no wasted words. Every clause adds operational value: exact output fields, first-step usage, and filtering guidance.

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?

For a simple tool with two optional parameters and no output schema, the description fully explains what the response contains and why the agent should call it first. The sibling relationship to query_metrics is implied clearly enough for correct tool selection.

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?

The input schema already documents both parameters fully, including case-insensitive search behavior and an explicit category enum. The description only restates 'filter by category or search by keyword,' adding no meaningful parameter-level detail beyond the schema.

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 uses a specific verb ('List') and resource ('analytics metrics'), then enumerates the exact contents returned: name, label, category, granularities, retention, and dimensions. It also positions the tool as the discovery entry point, distinguishing it from the sibling query_metrics tool.

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?

It explicitly instructs 'Call this first to pick a metric and check which granularity and dimensions it accepts,' which is clear when-to-use guidance. It also advises using filters to keep output small, but it does not explicitly name when-not-to-use or alternate sibling tools.

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