Transcript

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

Goes well beyond the annotations by disclosing credit consumption, the hard `confirm=true` requirement, the 2-minute ceiling and 10-credit cost of the AI fallback, and the fact that the read-like POST does not publish to social platforms. These are exactly the behavioral facts an agent needs before invoking a paid endpoint.

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?

Front-loads what is extracted and returned in the first sentence, then adds the cost/limit constraint and the no-publish clarification. Two paragraphs, no filler, each sentence carries distinct information.

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 no output schema, the description supplies the return contract (id/url/WEBVTT transcript) as well as auth/credit prerequisites and length limits. Nothing material for correct invocation is missing.

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%, and the input schema already documents the AI fallback's 2-minute limit and 10-credit cost as well as the `confirm` requirement, so the description largely restates structured data. It adds the framing that only the fallback is length-limited, but does not explain `account` or `language` beyond the schema.

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?

States a specific verb and resource ('Extracts existing transcripts, captions, or subtitles from a TikTok video by URL') and distinguishes itself from metadata-style siblings like tiktok_video_info. The return shape (`id`, `url`, `transcript` as WEBVTT) further pins down the purpose.

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?

Gives clear conditional guidance: existing transcripts work for any length, while the AI fallback only applies to videos up to 2 minutes and costs 10 credits when `use_ai_as_fallback=true`. It does not contrast itself against a sibling tool, but for a leaf extraction tool the when-to-use condition is well covered.

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