extract_pyperf

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

The description does not disclose any behavioral traits beyond the bare action 'extract'. Annotations are all false, providing no useful signals, so the description carries the full burden. It omits side effects, permissions, rate limits, or return behavior, and the verb 'extract' implies read-only, but no explicit statement is made.

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 a single concise sentence of nine words, front-loaded with the verb 'Extract'. It contains no filler or redundancy, making it highly scannable for an AI agent.

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?

With one parameter and an output schema present, the description should clarify context but does not. It fails to explain what constitutes 'evidence', the nature of a pyperf run, or how this relates to sibling extraction tools. The output schema might cover return values, but the description still lacks essential context for selection and invocation.

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 has a single parameter run_id with no description (0% schema coverage). The description mentions 'pyperf run' but does not explain what run_id refers to, its format, or how to obtain it. It adds minimal value beyond the parameter name.

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 extracts evidence from pyperf runs, naming specific evidence types (run, warmup, loop, value). It distinguishes from sibling extract_* tools by explicitly mentioning pyperf, but 'value evidence' is somewhat vague and could be more specific.

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 is provided on when to use this tool versus alternatives like extract_pytest or extract_perfetto. There are no usage scenarios, prerequisites, or exclusions mentioned, leaving the agent to guess based on the name alone.

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