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set_transcriber

Change the transcription engine used to convert video links into Markdown notes. Select from local (fast-whisper, mlx-whisper) or cloud (groq, bcut, kuaishou) options, with model size specification.

Instructions

切换转写引擎。

transcriber_type: fast-whisper(本地,需下载模型)/ groq(云端)/ bcut / kuaishou / mlx-whisper。
切到 fast-whisper 时可用 whisper_model_size 指定模型尺寸(tiny/base/small/medium/large-v3)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
transcriber_typeYes
whisper_model_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries burden for behavior disclosure. It discloses that fast-whisper requires a model download and that whisper_model_size only applies to that engine. However, it does not state whether the switch persists across sessions, affects ongoing tasks, or requires prior model installation for other engines. These gaps leave some behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the primary action, and each sentence adds distinct value: the first defines the tool and its options, the second clarifies a conditional parameter. No redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple setter tool with two parameters and a straightforward purpose, the description covers the main function, the valid values, and the conditional behavior of the optional parameter. The output schema exists and need not be explained. The sibling context further clarifies this tool's place in the workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description compensates by enumerating valid values for transcriber_type and explaining that whisper_model_size is optional and only relevant when switching to fast-whisper, with a list of model sizes. This adds meaningful guidance beyond the bare string fields in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear verb '切换转写引擎' (switch transcription engine), specifying the exact resource being modified. It lists distinct engine options (fast-whisper, groq, bcut, kuaishou, mlx-whisper), differentiating it from sibling tools like get_transcriber_config or download_transcriber_model, which query or download rather than switch.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on when to use this tool: whenever switching between transcription engines. It further clarifies that switching to fast-whisper may require prior model download and that whisper_model_size is available only in that case. It does not explicitly mention alternatives or exclusions, but the sibling set makes the intended use unambiguous.

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

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