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

Annotations already declare readOnly, idempotent, non-destructive and open-world. The description adds genuinely useful behavior — that the response is ranked, scored, explainable, and audited — but says nothing about the orchestration's cost/latency or what happens to the intermediate extraction/index artifacts, which matters most for a multi-stage tool.

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?

A single front-loaded sentence that enumerates the pipeline stages and the outputs in a logical order; slightly dense but every clause carries information.

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?

Output schema and annotations are present, so return shape and safety need no repetition. Still missing for an end-to-end orchestrator: expected cost/latency, and guidance on when to prefer the granular siblings instead of this heavier call.

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 description coverage is 100% and both parameters carry their own descriptions, so the schema does the work. The description adds no semantics about the resume/JD inputs, their expected granularity, or why both are marked 'Required' in text while the schema lists no required fields.

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 names a composite pipeline (extraction, indexing, taxonomy normalization, matching) and the concrete return (ranked candidates or jobs with scores, explanations, audit trail). This clearly separates it from single-step siblings like extract_resume_data or score_resume_against_jd, though it never names them explicitly.

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?

Usage is only implied: 'from a single call' hints that this is the one-shot alternative to chaining the granular sibling tools, but there is no explicit when-to-use/when-not statement or named alternative.

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