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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds genuinely new behavior: per-call pricing of ¥0.1, billing only on success with automatic refund on failure or empty results, and a pagination depth ceiling imposed by platform visibility. The only omission is what an exhausted cursor looks like, but the upstream-stops-returning note covers that adequately.

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?

Purpose is front-loaded, followed by return fields, use cases, pricing, and limits — a logical order with no filler. It is slightly dense in four sentences, but each carries distinct information.

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 return fields (text, likes, reply count, nickname, region, timestamp) and covers billing and pagination limits. What remains missing is disambiguation from tw_sub_comments, which matters given the sibling list.

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 tweet_id, cursor, and _token are already documented. The description only adds that results are paged and that paging depth is capped, which informs cursor semantics but gives no format hints beyond the schema. 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?

States a specific verb and resource ('获取推文评论（回复）') and enumerates the returned fields, so the agent knows exactly what it gets. It does not distinguish itself from the sibling tw_sub_comments (nested replies), so the boundary between top-level comments and sub-comments is left unclear.

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?

Names three concrete use cases (sentiment analysis, competitor reputation comparison, public-opinion monitoring), which implies when to reach for it. However, there is no explicit when-not-to-use guidance and no routing to tw_sub_comments for reply threads, which is the main sibling ambiguity.

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