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

Annotations already declare readOnly, idempotent, non-destructive and open-world, so safety is covered. The description goes well beyond that with site-by-site coverage (LinkedIn, Greenhouse, Lever, Ashby, SmartRecruiters, Workable, Workday, careers pages), the concrete failure mode of sites blocking reads, and the guidance that EEO, legal, consent, ai-use and trap questions are left for the user to answer.

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?

Front-loaded with the trigger and the return shape, then failure handling, then the optional flag. The platform list and question-kind list are the longest stretches, but both are decision-relevant rather than filler; only a slight trim would improve it.

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?

There is no output schema, and the description fully compensates by naming the returned fields and their format, plus what the optional questions payload contains. Combined with the failure path and downstream routing, an agent has everything needed to call this and to handle a blocked site.

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, but the description still adds decision-relevant meaning: it clarifies that questions only appear 'where the site publishes them' and that the flag is limited to certain platforms. The added value is real but modest, since the schema already enumerates the question kinds.

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: read a public job posting from a link and return title, company, location and full description as plain text. It also distinguishes itself from the sibling pipeline tools by naming match_job, tailor_resume, keyword_scan and interview_questions as downstream consumers, so an agent knows this is the ingestion step, not the analysis step.

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?

Gives an explicit trigger ('when the user gives a job posting link instead of its text'), an explicit fallback when the tool fails ('ask the user to paste the text'), and routes the output to specific sibling tools. The implied exclusion is clear too: if the user already pasted text, this tool is unnecessary.

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