extract_vocabulary

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

No annotations are provided, so the description must carry the transparency burden. It discloses that the output includes definitions and examples, which is useful behavioral information. However, it does not mention side effects, limitations (e.g., behavior when text is too long), or how min_band affects output, leaving some gaps.

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 sentence with no filler words, front-loading the verb and target output. It is appropriately concise, though it could add a bit more context without becoming verbose.

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 tool's low complexity, a full input schema, and the presence of an output schema, the description is largely complete. It states the core function and output contents, though it could mention when to use it relative to analyze_text for fuller context.

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 input schema has 100% coverage and fully describes all three parameters (text, count, min_band), including defaults. The description adds no additional meaning to these parameters beyond the schema, so baseline 3 is appropriate.

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 identifies the resource ('IELTS-relevant vocabulary with definitions and examples'), making the purpose clear. It does not explicitly differentiate from sibling tools like analyze_text, but the domain-specific focus on IELTS vocabulary provides reasonable distinction.

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 for extracting IELTS vocabulary from provided text, but does not state when to use this tool versus alternatives such as analyze_text, nor does it mention any exclusions or prerequisites. The context is clear enough for a basic use case but lacks explicit guidance.

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