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

Goes well beyond the readOnly/idempotent annotations by disclosing the pricing model (¥0.2 per call, billed only on success, refunded on failure or empty result) and a real retrieval constraint: pagination depth is capped by the platform's visible range and the upstream stops returning past it. Those are exactly the operational facts an agent needs before spending a call.

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?

Front-loads the purpose, then return fields, then use cases, then cost and limits — a natural decision order for an agent. Every sentence carries distinct information; nothing is padded or repeated.

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?

With no output schema, the description compensates by listing the returned fields, and it also covers billing and the pagination ceiling. For a simple three-parameter read tool, an agent has everything needed to decide and call correctly.

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 coverage is 100%, so page, bv_id and _token are already documented, and the baseline is 3. The pagination-depth caveat indirectly informs how far the page parameter is worth advancing, but no format or syntax detail is added beyond the schema.

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?

States a specific verb and resource (获取B站视频评论) and enumerates the returned fields (comment text, like count, reply count, commenter nickname/region, timestamp), which lets an agent distinguish it from the adjacent sub-comment and post-detail siblings without opening a schema. It never names those siblings explicitly, so differentiation is inferred rather than stated.

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?

Gives concrete use contexts (sentiment analysis, competitor reputation comparison, public-opinion monitoring) that tell an agent when this tool fits. It stops short of exclusions or alternatives — nothing says to use bili_sub_comments for replies or bili_post_detail for the video itself.

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