ae_transcribe_video

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

With no annotations available, the description carries the burden of behavioral disclosure and does so well: it states that the tool writes a JSON file to a specific path, describes the shape of the output, and lists prerequisites. It does not mention potential long-running behavior, model download side effects, or the exact return value, but the core behavioral traits are transparent.

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 tightly crafted sentences cover purpose, output location, dependencies, and downstream usage. Every sentence adds necessary context without redundancy or fluff.

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 medium-complexity tool with no output schema, the description covers the essentials: what it produces, where it stores it, prerequisites, and the intended downstream workflow. It could be more explicit about the exact return value or timing behavior, but it provides enough to guide correct invocation.

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 path, model, and language. The description adds no additional parameter-level meaning beyond the schema, which matches the baseline for high schema coverage.

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 specific action and resource: 'Transcribe a video/audio file with timestamps'. It clearly differentiates the tool from siblings by naming the downstream consumer (ae_transcript_to_cuts) and describing the output format (segments + word-level).

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 provides concrete usage context: required external dependencies (Python, faster-whisper/openai-whisper, ffmpeg), where output is saved, and how the result should be used downstream with ae_transcript_to_cuts. It lacks explicit when-not-to-use or exclusion guidance versus similar media-analysis siblings, so it stops short of full routing clarity.

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