parse_jd

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

No annotations are provided, so the description carries the full burden. It discloses that it uses Claude API internally, but does not mention side effects, authentication needs, or rate limits. The return format (structured JSON) is stated, but behavioral boundaries are not fully covered.

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 with no wasted words. The first sentence lists output fields, the second explains internal API and purpose. Front-loaded and efficient.

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 no output schema, the description adequately covers return values by listing structured fields. It mentions internal API usage. It could improve by noting input size limits or error handling, but is largely complete for a parse tool with one parameter.

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. The description adds 'the full job description text', but this does not go beyond the schema description. 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 verb 'parse' and the resource 'job description', listing specific structured outputs (hard requirements, nice-to-haves, responsibilities, etc.). This distinguishes it from sibling tools like 'analyze_fit'.

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 implies usage for extracting structured signals before fit analysis, but lacks explicit when/when-not guidance or alternatives. No mention of when not to use this tool vs. siblings like 'analyze_fit'.

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