Score a resume against a job

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

Annotations already establish readOnly, idempotent, closed-world behavior, so the description does not need to argue safety. It adds real value by disclosing the response content (score plus per-dimension breakdown and matched/missing skills), which matters because no output schema exists. It does not say whether inputs are normalized or how ties/partial data are handled, keeping it below a 5.

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 packs the purpose, the scoring range, the breakdown dimensions, and the skill-diff output with no filler. Every clause earns its place.

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 no output schema, the description carries the return-value burden and does so reasonably by enumerating the breakdown dimensions and matched/missing skills. What is missing is routing versus the overlapping siblings predict_match and rate_matches, which matters in this family of scoring tools.

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 top-level parameters are documented in the schema, including the note that resume may be plain text or the JSON returned by parse_resume. The description adds nothing beyond that, so the baseline 3 applies.

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?

States a specific verb and resource (score a resume against a job posting) and also names the output scale (0-100) and breakdown dimensions, so the agent knows exactly what it gets. It is distinguishable from parse_resume/parse_job_posting, which produce inputs rather than scores.

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 implied by the name and description: use it when you have both a resume and a job and want a fit score. However, siblings predict_match and rate_matches plausibly overlap with this, and the description gives no when-to-use/when-not guidance or prerequisite chain (e.g. call parse_resume first) to disambiguate.

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