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

The annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered elsewhere. The description still adds genuinely useful behavior: reverse-chronological ordering, the fields returned, and a billing rule (¥0.3 per call, charged only on success, with automatic refunds on failure or empty results). It does not disclose pagination limits or rate constraints, which is the main remaining gap.

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?

Four short sentences, front-loaded with the purpose and return contents before the usage and billing notes. There is no filler, though the billing sentence could arguably be separated via annotations. The density is high but readable.

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?

Because there is no output schema, the description usefully specifies what a result contains and how results are ordered. Combined with the annotations, an agent has enough to call the tool correctly. The remaining gap is pagination and volume behavior, which is only hinted at by the cursor parameter.

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 user_id, cursor, and _token are already documented in the schema. The description adds nothing about parameter semantics and does not mention the cursor or how paging works, so it provides no meaning beyond the structured data. The baseline score of 3 is appropriate here.

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 opens with a clear verb and resource ("获取快手用户视频列表") and states the return shape (reverse-chronological content list with content IDs and basic engagement metrics). It is distinguishable from ks_user_info and ks_post_detail by implication, but it never names a sibling or explicitly contrasts itself with them.

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?

It gives concrete use cases: monitoring an account's posting frequency, studying content strategy, or bulk-collecting that account's content IDs. That is real when-to-use guidance, but there are no exclusions and no pointer to an alternative such as ks_keyword_search when the user instead wants to find accounts by topic.

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