classify

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

The description discloses a key behavioral trait: the text is classified into exactly one of the provided labels, which is beyond what the schema states. With no annotations available, it carries the full disclosure burden, but it does not cover edge cases like ambiguous texts or the exact return format, 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, well-structured sentence that front-loads the verb 'Classify' and provides a compact example. There is no fluff or repetition, making it highly efficient.

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 simple two-parameter tool without output schema or annotations, the description is largely sufficient. It explains the task, the 'exactly one' constraint, and the user-supplied labels. It could be improved by explicitly stating the return value (e.g., the chosen label), but the behavior is reasonably inferable.

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 already provides 100% parameter description coverage, with clear definitions for 'text' and 'labels'. The description adds no new parameter-level information beyond reinforcing that labels are user-supplied, so the baseline of 3 (with full schema coverage) applies.

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 ('Classify') with a clear object ('a text') and defines the scope ('exactly one of the candidate labels you provide') with examples (sentiment, topic, intent). This clearly distinguishes it from sibling tools like summarize or extract_entities, which perform different NLP tasks.

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 clearly implies when to use the tool: when you need to assign a text to one of a set of user-defined categories. However, it does not explicitly mention when not to use it or point to any alternative tools, lacking exclusions that would make it a 5.

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