nlp_answer

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds little behavioral context: it only states that the return is 'indicator data' without explaining what that means, how results are structured, or any constraints. No additional traits like rate limits or authentication needs are disclosed.

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 brief but not effectively structured. It repeats the Chinese title, includes a URL that is unlikely to help an AI agent understand usage, and the param/return documentation is terse. It does not front-load a clear functional explanation, and the content is under-specified rather than appropriately concise.

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 simplicity (one parameter, no output schema), the description still feels incomplete. It fails to explain what the tool does in practical terms, what 'indicator data' consists of, or how it relates to the sibling 'nlp_ownthink'. An agent would struggle to know when to call this tool and what to expect in the response.

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 has one parameter 'question' with type string and a default, but no property description. The description adds that the question should be a 'word in chinese', which provides useful language guidance beyond the schema. However, it does not clarify whether a full sentence is acceptable, and the term 'word' is potentially restrictive. The return type is mentioned but not tied to parameter semantics.

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 starts with '智能问答' (Intelligent Q&A), which is simply the tool's title restated, and provides a URL to ownthink.com. It does not state a specific verb+resource action, such as 'answer a question using the OwnThink robot'. The return type 'indicator data' is vague and does little to clarify what the tool actually does, and it fails to distinguish itself from the sibling tool 'nlp_ownthink'.

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?

No guidance is given on when to use this tool versus alternatives like nlp_ownthink. The description only provides parameter documentation and a return type, with no mention of appropriate use cases, prerequisites, or exclusions. It is not misleading, but it provides no usage direction.

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