优化简历

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

With no annotations, the description carries the full burden and does well: it discloses the credential-dependent branching, the mode=host-llm fallback payload, and the honesty constraint against fabricating employers or numbers. Missing details are minor (no note on output format length, cost, or latency of the LLM path).

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 core action and the honesty constraint are front-loaded, and the bilingual restatement is compact rather than padded. The Chinese and English halves overlap considerably, which is slight redundancy but not enough to hurt selection.

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 three-parameter, no-annotation, no-output-schema tool, the description covers what matters: what it does, how it behaves with and without API keys, and what comes back in each mode. An agent can call it correctly without further reading.

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 the schema documents all three parameters (jd, content, extra_info) in Chinese. The description only gestures at parameters indirectly via 'JD alignment' and the no-fabrication rule; it adds no syntax, format, or priority detail beyond the schema, so 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+resource (polish/optimize a resume) and qualifies the scope with concrete techniques — STAR, quantification, JD alignment — plus the hard constraint 'never fabricates employers or numbers'. It does not explicitly name a sibling to contrast with (analyze_resume is the closest overlap), so it stops short of a 5.

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?

It explains the two execution paths clearly: call the LLM directly when DEEPSEEK_API_KEY/OPENAI_API_KEY is configured, otherwise return mode=host-llm with instructions for the host model. That is useful environmental context, but there is no guidance on when to pick this tool over analyze_resume or import_resume, so usage is only implied.

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