Generate Cover Letter

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

Beyond the annotations (readOnlyHint false, destructiveHint false), the description discloses that the tool reads the saved resume, generates a letter, returns it, and saves it to the account. It also mentions the paid-plan prerequisite. This goes beyond the annotations by explicitly stating the write side-effect (saving) and the dependency on an existing resume, providing meaningful transparency about the tool's behavior.

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 well-structured. It leads with the primary action and context, then covers quality attributes, the return value, the save behavior, and the prerequisite—all in three sentences with no redundancy or unnecessary detail. The information is efficiently packed and front-loaded, making it easy for an agent to parse the core purpose quickly.

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?

The description covers the essential aspects: purpose, input (resume from account), output (returned letter), side effect (saving), and a key constraint (paid plan). It does not mention output format, error handling, or specific edge cases, but for a straightforward cover-letter generator, the provided context is sufficient for an agent to understand when and how to use it. The inclusion of the saved-resume dependency and the paid-plan requirement adds practical completeness.

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?

The schema already provides full descriptions for both parameters (company_name and job_description), achieving 100% coverage. The tool description does not add any additional semantic detail beyond what the schema specifies—it references 'a specific job' but does not elaborate on parameter meanings, constraints, or interdependencies. Since schema coverage is high, the baseline of 3 applies, and the description adds no incremental value for parameter understanding.

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 primary action—writing a cover letter for a specific job—and specifies the source (resume on the user's account), the style (own voice, no clichés), and the output (returns the letter). It also notes the side effect of saving, making the purpose unmistakable and distinct from sibling tools like resume optimization or interview question generation.

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 implies when to use the tool (when a cover letter for a job is needed) but does not explicitly contrast it with alternatives or provide conditional guidance. Sibling tools such as optimize_resume or generate_interview_questions are not referenced, so users must infer the appropriate choice from the stated purpose. The paid-plan requirement is mentioned, which is useful context, but it does not constitute usage guidance relative to other tools.

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