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

The annotations already declare readOnly/idempotent/non-destructive/openWorld, so the description correctly focuses on non-structural traits: cost (¥0.1/次), billing only on success, and automatic refund on failure or empty results. Since no output schema exists, listing the returned metrics also carries real behavioral weight. Missing only notes on rate limits or auth, the latter partially covered by the _token schema field.

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?

Three tight sentences ordered as purpose, return values, usage, then cost — front-loaded with the most decision-relevant information and no filler. Every sentence contributes a distinct piece of 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?

For a two-parameter read tool with full annotation coverage and no output schema, the definition covers purpose, return payload, usage context, and pricing. It is nearly complete; only auth expectations and any rate/error behavior beyond the refund note are left unaddressed.

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 description coverage is 100% (bv_id as BV号, _token as fallback auth), so the schema already documents both parameters. The description adds no format or syntax detail for bv_id beyond what the schema states, making the baseline 3 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?

The description names a specific verb and resource (获取B站视频详情) and immediately enumerates the returned fields (标题、播放/弹幕/点赞/投币/收藏/分享数、UP主信息、分区、时长、发布时间), so an agent knows exactly what it gets. The phrase 单条内容 in the usage sentence separates it from the keyword-search and batch siblings in the same family.

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 three concrete use cases — single-item engagement analysis, asset ingestion, and as a starting point for batch tasks — which is clear context for selection. It does not explicitly name an alternative tool or a when-not condition (e.g., use bili_keyword_search for discovery), so it stops short of the top score.

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