video-transcript

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

With no annotations provided, the description carries the transparency burden. It discloses key behaviors: it works even without captions using server-side speech recognition, supports a wide range of platforms, and imposes a 30-minute limit. However, it stops short of describing the return format, error handling for unsupported URLs, or rate limits, leaving some gaps.

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 three sentences, each earning its place: the core action, the no-captions capability and supported sites, and the length limit. It is concise, front-loaded with the purpose, and free of wasteful detail.

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 tool with a single parameter, no annotations, and no output schema, the description provides adequate context: defines the input, lists supported sources, and states a key constraint. It doesn't explain the output format or failure behavior, but the return value ('transcript') is largely implied by the tool's name and the stated purpose.

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 schema provides no description for the 'url' parameter (0% coverage). The tool description compensates by specifying that the URL must be a video URL and enumerates compatible platforms (TikTok, X/Twitter, TED, Twitch, podcasts). This gives meaningful guidance, though it doesn't detail URL formatting or what happens with invalid inputs.

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 the tool extracts the spoken-word transcript from a video URL, using a specific verb ('Extract') and resource. It distinguishes itself from the sibling tool 'list_supported_platforms' by focusing on extraction rather than listing supported sites.

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 clear use context: it works when captions are unavailable via server-side speech recognition and supports many sites. It also notes the 30-minute max length, which implicitly sets when-not-to-use constraints. However, it doesn't name any alternative tools, so it falls short of a full 5.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses that there are 'current limits' and that it lists supported platforms, adding some context beyond the tool name. However, it does not specify what the limits apply to, what the return format is, or any other behavioral details.

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 a single sentence that is front-loaded with the verb 'List' and immediately conveys the resource and scope. Every word earns its place, with no redundancy or filler.

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 simple, zero-parameter informational tool, the description is adequate. It clearly states what is listed and mentions 'current limits' as part of the output. Without an output schema, a bit more detail about the return format could be helpful, but the description covers the essential context.

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 tool has zero parameters, so there is nothing for the description to explain about parameters. The baseline for 0-parameter tools is 4, and the description does not need to add param semantics when none exist.

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 uses the specific verb 'List' with the resource 'video platforms' and adds 'supported and current limits,' clearly indicating what the tool does. This distinguishes it from the sibling tool get_video_transcript, which retrieves transcripts rather than listing platforms.

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 implies that this tool is used to discover supported platforms and limits before using other tools like get_video_transcript, but it does not explicitly state when to use it versus alternatives or mention any exclusions. The usage context is implied rather than stated.

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