面试复盘

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

No annotations are provided, so the description carries the full disclosure burden. It usefully enumerates the report output (per-question diagnosis, optimized answers, weakness ranking, prep checklist), which tells the agent what a call yields. It does not state whether anything is persisted, whether the report is retrievable later, or any input limits — meaningful gaps for a tool with zero annotation coverage.

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 with the action first and the report contents compactly listed in parentheses. No filler, no redundancy with the title, nothing that fails to earn 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 compensates by enumerating what the generated report contains, and the input surface is small and simple (1 required, 1 optional). The remaining gap is behavioral: persistence, retrieval, and length/size constraints are unaddressed, but for a one-shot analysis tool this is close to sufficient.

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 both chatLog and the optional jdText are already documented in the schema, making 3 the baseline. The description adds no additional parameter meaning — it never mentions the optional JD parameter, which visibly shapes the quality of a review, so it does not improve on the structured data.

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: paste an interview transcript and generate a deep review report, with the report's components enumerated in parentheses. It clarifies the tool accepts either real or mock interview logs, which separates it from the live mock_interview_* tools. It does not, however, distinguish itself from the sibling interview_summary, which an agent could reasonably confuse it with.

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 rather than stated: '任意一场真实/模拟面试的对话记录' signals a post-interview, transcript-in-hand scenario. There is no explicit when-to-use, no exclusion, and no named alternative (e.g. interview_summary) to route the agent. Adequate but leaves the agent to infer selection.

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