fetch_transcript

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

With no annotations provided, the description carries the full disclosure burden and does substantial work: it reveals caching behavior by language preference, retry policy (only transient errors), default output representation, and that responses include provenance, caption type, and a verifiable content hash. The only notable gaps are error/edge-case behavior (e.g., missing transcript) and any rate-limit or auth constraints.

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?

Six short sentences, each carrying distinct information: purpose, format options, default rationale, caching, retry behavior, and response contents. The core purpose is front-loaded and there is zero redundancy or filler — every sentence earns its place.

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 9-parameter tool with no annotations and no output schema, the description is remarkably complete: it covers purpose, formats, default behaviors, caching, retry semantics, and key response elements. Remaining gaps — error handling for missing transcripts and any rate-limit/authentication context — are minor given how much behavioral ground is already covered.

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 coverage is 100%, establishing a baseline of 3. The description adds genuine value beyond the schema by explaining the 'why' behind defaults — 'Defaults to segments-only for token efficiency' maps to the output parameter, and 'Cached by language preference' illuminates the interplay between languages and bypass_cache. This rationale helps the agent reason about parameter choices.

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 opening sentence 'Fetch a YouTube video transcript' uses a specific verb and clearly identifiable resource. It is further enriched by specifying return formats (compact JSON or markdown), default output mode, and content guarantees (provenance, caption type, content hash), leaving no ambiguity about what the tool does.

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 gives implied usage context through 'Defaults to segments-only for token efficiency' and 'Cached by language preference,' which signal this is an efficient default path for transcript retrieval. However, there is no explicit when-to-use guidance, no exclusions, and no alternative tools to route toward — though this is partly mitigated by there being no sibling tools listed.

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