get_transcript

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

Annotations already cover the read-only, open-world, non-destructive profile, and the description adds genuinely new operational facts: Whisper may take several minutes, downloads a model on first use, and the returned content is untrusted. The latency and prompt-injection warnings are exactly the kind of disclosure annotations cannot carry.

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 output/scope statement is front-loaded in the first sentence, followed by tight, non-redundant notes on source behavior, language handling, and latency. The line-broken phrasing is terse and every sentence carries information, though the fragmentary style slightly reduces readability.

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 an output schema present, the description correctly omits return-value detail and instead covers source semantics, language preference, latency, and content trust. It stops short of describing failure modes (no captions available, invalid video ID) or any auth/quota considerations, which leaves a small completeness gap.

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 0%, so the description must compensate, and it does for the two non-obvious parameters: source=auto is defined as captions-then-Whisper fallback, and languages is clarified as an ordered caption preference (default en, hi) rather than a translation request. The 'video' parameter is only implicitly covered, which is the main remaining 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 first sentence gives a specific verb (Return) plus concrete resource (transcript text and timestamped segments) and input domain (YouTube URL or video ID), which is far more informative than the tool name alone. It does not, however, distinguish itself from the sibling list_captions, so an agent must infer the boundary itself.

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 states a clear selection rule for the source parameter: use source=captions to avoid audio downloads and model inference, while source=auto tries captions first then Whisper. That is actionable when-to-use guidance, though it offers no explicit routing advice against the siblings list_captions or get_status.

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