generate_resume_for_job

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'creates a customized version' and 'returns JSON resume data', but lacks critical details: whether this is a read-only or mutation operation, if it requires specific permissions or credits, what happens to existing resumes, or any rate limits. For a tool that likely involves AI processing and data generation, this is insufficient.

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 concise and front-loaded, stating the core purpose in the first sentence. The second sentence elaborates on customization, and the third specifies the return format. There's minimal waste, though it could be slightly more structured (e.g., separating behavioral notes).

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?

Given the complexity (AI generation, 5 parameters) and lack of annotations or output schema, the description is moderately complete. It covers the basic purpose and return format but misses key contextual details: prerequisites, behavioral traits (e.g., mutation effects, costs), and how it integrates with sibling tools. Without an output schema, it should ideally describe the JSON structure more.

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%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema—it doesn't clarify parameter interactions, default behaviors beyond the schema's 'defaults' note, or examples of use. The baseline of 3 is appropriate when the schema does the heavy lifting.

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 tool's purpose with specific verbs ('generate', 'creates', 'tailored', 'highlighting') and resources ('AI-optimized resume', 'specific job application'). It distinguishes itself from siblings like 'get_resume' or 'upload_resume' by focusing on generation and customization rather than retrieval or upload.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing resume or job application), nor does it differentiate from siblings like 'get_generated_resume' (which might retrieve previously generated resumes) or 'update_application' (which might modify job applications).

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