获取 YouTube 视频转录

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

The description goes well beyond the annotations (readOnlyHint=false, openWorldHint=true) by disclosing that results are written to a file, cached for repeated calls, and that cache invalidation occurs on model/hotword changes. It also explicitly warns that the tool returns only metadata + preview + hints, not the full transcript. This is rich, non-contradictory behavioral context.

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 well-structured with three focused paragraphs: pipeline, model quality, and return-value warning. It is longer than strictly necessary because some details duplicate the schema, but every section adds meaningful context and the key purpose is front-loaded.

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 and no sibling tools, the description carries full responsibility for return-value and artifact behavior. It covers the saved file path, cached result behavior, preview length semantics, model behavior, and the fact that the full transcript is not returned directly. An agent has enough context to invoke the tool and interpret its result 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 description coverage is 100%, so the baseline is 3. The description adds value on top by explaining cross-parameter cache behavior ('先用 small 试、再用大模型重跑'不会重复下载), model speed/accuracy tradeoffs, and how output_language only affects summaryHint. This supplements rather than merely repeats 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?

The description opens with a clear verb+resource statement: '把 YouTube 视频转成文字稿' (convert YouTube video to transcript), and explains the two-mode pipeline (existing subtitles first, local faster-whisper fallback). This makes the tool's function unmistakable even without sibling tools.

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?

There are no sibling tools to contrast with, but the description gives clear usage context: it distinguishes when subtitles are used vs when audio transcription is needed, and offers concrete model-selection guidance ('要快速草稿可传 model="small"'). It does not explicitly state when not to use the tool, but that is a minor gap given no alternatives exist.

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