interview_prediction

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

With no annotations provided, the description carries the disclosure burden. It clearly discloses the membership requirement ('需会员') and what the response contains (reference answers, answering ideas, and follow-up questions). It does not explicitly discuss non-mutating behavior, but the predictive phrasing makes that evident; minor gaps remain around failure behavior for non-members, but the key constraints are visible.

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?

The description is two compact sentences with no filler: the core purpose is front-loaded, the deliverables are listed in one clause, and the membership gate is stated last. Every sentence 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?

For a generation tool with a required company and job title, the description is largely complete: it names the inputs from the schema, describes the output components, and flags the membership constraint. Because there is no output schema, it wisely describes the return content. It could add guidance on the optional resumeText personalization or the default count, but those are covered by the input schema.

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 coverage is 100%, so the baseline is 3. The description's phrase '根据公司与岗位' mirrors the required companyName and jobTitle parameters but does not add extra meaning about count or resumeText; the schema already documents those. The description neither compensates beyond schema nor omits critical parameter context.

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 states a specific action ('预测可能被问到的面试题'), the resource (interview questions tailored to a company and job title), and the deliverables (reference answers, answering approaches, follow-up questions). This clearly distinguishes it from sibling tools like interview_questions_bank, which suggests a generic question repository rather than company-specific prediction.

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 when to use the tool: when a user wants predicted interview questions for a specific company and job title, and it warns that membership is required. However, it does not provide explicit when-to-use/when-not-to-use guidance or mention alternatives such as interview_questions_bank or interview_coach, so an agent must infer the appropriate routing.

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