transcribe_video

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

With no annotations, the description fully covers behavior: downloads audio, transcribes locally with Whisper, returns timestamped transcript, caches results, and auto-downloads models. It also lists model sizes with trade-offs, ensuring the agent understands resource usage and performance.

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 a concise purpose, logical breakdown of behavior, caching, model sizes, sample prompts, and parameter details. Every sentence adds value, and the front-loaded purpose ensures quick understanding.

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?

Given the output schema exists, the description does not need to detail return values. It covers usage, parameters, caching, and model options comprehensively. The only minor omission is potential rate limits or disk space, but overall it is complete.

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?

The input schema has no descriptions (0% coverage), but the 'Args:' section in the description adds critical semantics: 'url' supported by yt-dlp, 'model_size' options with defaults, and 'language' auto-detection. This fully compensates for the schema gap.

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 'Download and transcribe a YouTube video (or any video URL) with timestamps,' specifying the verb, resource, and output. It distinguishes from sibling 'transcribe_local' by focusing on URLs rather than local files.

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 multiple sample prompts that trigger the tool, covering various use cases like transcription, summarization, and timestamp querying. It also explains when to use different model sizes based on speed/accuracy needs, though it does not explicitly mention when not to use or alternative tools.

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