post_job

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

With no annotations provided, the description carries full burden for behavioral disclosure. It reveals several important behavioral traits: the job appears in multiple channels, has a 30-day default validity period, and that work experience is converted to days internally. However, it doesn't disclose critical mutation implications like whether this is idempotent, what permissions are needed, or what happens on failure. The description doesn't contradict any annotations since none exist.

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 appropriately sized and well-structured with clear sections: a purpose statement, three bullet points showing where jobs appear, and a '提示' section with three operational tips. Each sentence serves a purpose, though the third bullet about WonderCV channels could be more specific. The information is front-loaded with the core purpose stated first.

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 mutation tool with 13 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and some behavioral aspects (where jobs appear, default validity, internal format conversions), but doesn't address error conditions, response format, authentication requirements, or detailed parameter interactions. Given the complexity, it should provide more complete operational context.

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?

With 100% schema description coverage, the baseline is 3. The description adds minimal parameter semantics beyond the schema - it only mentions the 'require_ai_fluency' parameter in the tips section to explain its effect ('优先展示给 AI-Agent 熟练的候选人'). It doesn't provide additional context for other parameters like salary ranges, experience conversions, or the session_id requirement from 'register_company'.

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 clearly states the action ('发布职位' - post job) and target ('ClawHire 招聘市场'), providing a specific verb+resource combination. It distinguishes this from sibling tools like 'list_jobs' or 'search_candidates' by being a creation/write operation rather than a read operation. However, it doesn't explicitly contrast with 'register_company' which is another write operation.

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 provides some implied usage context by listing where the job will appear (ClawHire search, candidate recommendations, WonderCV channels), suggesting this is for broad job posting. However, it doesn't explicitly state when to use this tool versus alternatives like 'request_outreach' or provide clear exclusions. The '提示' section offers operational tips but not comparative guidance.

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