get_metrics

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

No annotations are provided, so the description must disclose behavior. It mentions the output formats and content but does not discuss idempotency, rate limits, authentication requirements, or whether the operation is read-only. For a metrics tool, the lack of 'safe to call' indication is a gap.

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 that efficiently convey purpose and content. No fluff, every word adds value.

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 one optional parameter and no output schema, the description is adequate. It lists the metric categories. It could be improved by noting if metrics are real-time or cached, but overall it provides sufficient context.

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 one parameter 'format' described in the schema. The description adds the phrase 'Prometheus text format or JSON', which aligns with the enum, adding minimal extra meaning. This meets the baseline of 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 tool gets server metrics in two formats (Prometheus text or JSON) and lists the types of metrics included (request counts, latency, queue stats, etc.). This is specific and distinguishes it from sibling tools like list_loaded_models which deal with models.

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?

No guidance on when to use this tool vs alternatives. While it is obvious for monitoring, the description does not provide context such as 'use for performance monitoring' or exclude cases like debugging.

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