boss_ai_cover_letter

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

With no annotations, the description carries full burden; it does disclose the key behavioral trait that output is draft-only and never sent, which prevents false expectations of an apply action. It omits what the draft contains, length, formatting, and failure behavior when job_id/jd_text is missing.

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 pairs the action with its inputs and the draft-only constraint; the bracketed availability tag is compact. Minimal waste overall.

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 moderate-complexity generative drafting tool with no output schema, the description covers purpose, inputs, and the critical not-sent constraint. Missing only draft-format expectations and error/enumeration behavior, which are 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 coverage is 100% with per-field Chinese descriptions and enums for lang and tone, so the schema already carries parameter meaning. The description only echoes the resume/job inputs and adds no syntax or precedence detail (e.g. job_id vs jd_text exclusivity).

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 (起草) and resource (求职信/自我介绍), plus scope (based on local resume and target job). It clearly distinguishes itself as a drafting tool, though it does not name which sibling handles adjacent tasks like optimization or suggestions.

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 availability tag (roles=candidate, candidate_platforms=zhipin) gives real routing context, and 'based on local resume and target job' implies the input precondition. However, no explicit alternatives are named (e.g. boss_ai_suggest, boss_ai_optimize) and there is no when-not guidance.

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