extract_skills_structured

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

Description adds significant behavioral detail beyond annotations: no AI used, algorithmic methods (NER, TF-IDF, section detection), frequency-based proficiency estimation. It also discloses the output structure (13 categories, proficiency levels, supporting evidence). No contradiction with readOnlyHint=true.

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?

Three concise sentences. Front-loaded with purpose and method, then output structure, then usage comparison. No redundancy or fluff.

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?

No output schema, so description carries full burden of explaining return format. It lists categories, proficiency levels, and supporting evidence—sufficient for an agent to understand the output. Minor gap: no mention of edge cases or confidence metrics, but overall complete enough.

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 clear descriptions for both parameters. The description does not add extra meaning beyond the schema; it focuses on output and methodology. 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?

Description clearly states the verb (extract and categorize), resource (resume text), and method (algorithmic analysis). It distinguishes from sibling extract_keywords by specifying structured, categorized, proficiency-rated output. The explicit list of 13 categories adds specificity.

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?

Directly compares to extract_keywords and advises using this tool for categorized, proficiency-rated output. Lacks explicit when-not-to-use guidance, but the contrast is clear enough for an agent to decide.

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