list_metrics

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

No annotations exist, and the description fails to disclose behavioral traits such as pagination behavior, default sorting, or what happens when parameters are omitted. The description adds minimal behavioral context beyond the literal listing action.

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?

Two sentences, directly states purpose and one key condition. No extraneous information. Front-loaded with action and resource.

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 5 parameters, no output schema, and no annotations, the description covers the core purpose and one key parameter. It could mention pagination behavior or default sorting, but the schema compensates. Acceptably complete for its complexity.

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%, meaning each parameter is already documented in the schema. The description adds meaning only to 'include_builtin'. For other parameters, it adds no extra value, meeting the baseline 3.

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 verb 'List' and the resource 'available evaluation metrics'. It differentiates from sibling list tools by specifying 'evaluation metrics', distinct from agents, runs, test cases, etc.

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?

Provides a specific usage guideline with 'Set include_builtin=true to see built-in metrics'. While it doesn't explicitly mention when not to use or alternative tools, the context and name make it obvious this is for metrics only.

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