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

Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavioral context by noting that it requires a connected AI Applyd account and consumes the user's AI credits, and it summarizes what analysis outputs to expect. There is no contradiction with the annotations.

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 three sentences with no filler: the first states the core value, the second gives the call timing, and the third states the prerequisites. 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 single-parameter tool with full schema coverage and safety annotations, the description covers purpose, expected output categories, usage timing, and prerequisites. The absence of an output schema is mitigated by explicitly naming what the extraction returns.

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% and the sole parameter, job_description, already has a clear description in the schema: 'Full text of the job description.' The tool description reinforces the general use case but adds no new parameter-level detail beyond what the schema already provides.

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 uses a specific verb, 'extract,' and defines the resource as a job posting, then lists concrete output categories: ATS keywords, must-have versus nice-to-have requirements, seniority signals, and red flags. This clearly distinguishes it from sibling tools like aiapplyd_score_resume or aiapplyd_generate_cover_letter.

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 explicitly says to call this before tailoring a resume, which gives a clear actionable trigger. It does not explicitly mention when not to use it or name alternative tools, but the stated context is sufficient for correct use.

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