audio_transcribe

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds valuable contextual details: it uses faster-whisper small model with CPU-only INT8 quantization, and it returns segmented text and full text. This gives the agent a richer understanding of performance and output characteristics beyond the annotations.

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 concise, consisting of three focused sentences. It front-loads the core purpose, then adds model details, supported formats, and output type, with no unnecessary words. Every sentence adds meaningful info.

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?

Moderate complexity tool with no output schema, but the description explains what is returned (segmented text and full text). It covers supported formats and the CPU-only constraint. It doesn't mention error handling, file size limits, or prerequisites, but for a read-only transcription tool, these are less critical. Overall, it's sufficiently complete for an agent to select and invoke it.

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 schema already documents all three parameters (file_path, task, language). The description does not add additional parameter semantics—it only mentions supported formats and output, which are not directly tied to parameter usage. Thus, baseline score of 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?

The description clearly states the tool's function: '本地音频转写' (local audio transcription) using a specific model. It distinguishes from sibling tools (vision_analyze, vision_chat, tools_health) by focusing on audio-to-text conversion, making its purpose unambiguous.

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?

The description provides clear context that this is for transcribing local audio files, listing supported formats and model constraints. It doesn't explicitly mention alternatives or when-not-to-use, but the sibling tools are visually/health-oriented, so the usage context is clear enough without exclusions.

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