extract_experience_structured

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

Annotations already mark it as read-only (readOnlyHint: true). The description adds rich behavioral context: algorithmic-only processing, date range detection, metric extraction, NER classification, heuristic block splitting, and output details (structured entries, career statistics). This fully discloses the tool's behavior beyond what annotations offer.

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: the first states purpose and method, the second lists output content. No redundancy, no fluff. Information is front-loaded and every sentence 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?

Given one required parameter, no output schema, and no nested objects, the description covers the tool's purpose, method, and output fields. It could mention edge cases (e.g., empty input) but is otherwise sufficient for a focused extraction tool.

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 only parameter 'resumeText' has full schema coverage (100%) with a clear description ('The raw text content of a resume'). The description adds no extra semantics, but schema coverage is high, so 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 ('Extract and structure'), resource ('work experience from resume text'), and method ('algorithmic analysis only (no AI)'). It distinguishes from sibling tools like extract_skills_structured and parse_resume by focusing specifically on work experience and specifying a deterministic, non-AI approach.

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 algorithmic, non-AI extraction of work experience, but it does not explicitly state when to prefer this over siblings like parse_resume or extract_skills_structured. No exclusion criteria or alternative recommendations are provided, leaving the agent to infer context.

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