generate_job_description

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

No annotations are provided, so the description carries full burden. It discloses key behavioral traits: AI generation, bias analysis (score and warnings), and output format (text plus metadata). However, it lacks details on permissions, rate limits, error handling, or whether the generation is deterministic vs. stochastic.

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 a single, well-structured sentence that efficiently conveys core functionality (generation), method (AI), key features (inclusive, bias-free), and output components (text, score, warnings). Every element earns its place with zero waste.

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 no annotations, no output schema, and 5 parameters with full schema coverage, the description adequately covers purpose and output expectations. It could improve by addressing mutation implications (e.g., is this a read-only generation or does it store data?) or error cases, but it's largely complete for a generative AI tool.

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%, providing full parameter documentation. The description adds no parameter-specific semantics beyond what the schema already states (e.g., it doesn't explain how 'requirements' formatting affects output). Baseline 3 is appropriate when schema does 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 an inclusive, bias-free job description using AI') and resources (job description text, bias score, warnings). It distinguishes what the tool does (AI generation with bias analysis) without tautology or ambiguity, even without sibling tools for comparison.

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 usage context (creating job descriptions with bias awareness) but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. With no sibling tools, the bar is lower, but it lacks specific when/when-not instructions.

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