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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorld=false, so the safety profile is covered. The description adds meaningful substance beyond that: it discloses the review's components (quality signals, authorship checks, risky-function trace, probing questions) and that output is plain-English for a non-technical audience.

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 states the action, the audience, and the deliverables with no filler. The colon-list is dense but the length is appropriate and 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?

With no output schema, the description carries the burden of describing returns, and it does so by naming the four review components, so an agent knows what comes back. Given only two well-documented parameters and full annotation coverage, the remaining gap (e.g., length or depth of the review) is minor.

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%, so role and code_or_repo are fully documented in the schema itself, establishing the baseline of 3. The description echoes the input surface ('GitHub repo or code sample') but adds no format, length, or content guidance beyond what the schema already states.

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?

Names a specific verb and resource and enumerates the concrete outputs (quality signals, authorship checks, risky-function trace, revealing questions), which clearly separates it from siblings like detect_resume_injection or design_coding_assessment. An agent can tell what it does without opening the schema.

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?

States the target scenario explicitly ('for non-technical hirers' reviewing a candidate's GitHub repo or code sample), which gives a clear usage context. It stops short of naming when to prefer it over adjacent tools such as detect_resume_injection or design_coding_assessment, so no exclusions or alternatives are offered.

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