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CLIPCLIPER

Get chapters

get_chapters
Read-only

Split a public video into topic chapters (start/end seconds, title, one-line summary) from its transcript. Transcribes the video first if needed (same cost and async behaviour as get_transcript); the chapters themselves are free and cached.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic video URL: YouTube video/short/live replay, Twitch VOD, Kick VOD or TikTok video.
languageNoSpoken language of the video as ISO-639-1 (e.g. 'en', 'es'). Omit to auto-detect.
max_chaptersNoUpper bound of chapters (default 12).
wait_secondsNoSeconds to wait for processing before returning status 'processing' (default 90). Long videos can take several minutes: just call again.
output_languageNoLanguage for titles and summaries (ISO-639-1). Default: the language of the video.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare readOnlyHint and openWorldHint, so the description carries the burden of behavioral disclosure. It explicitly states that the video is transcribed first if needed (same cost and async behaviour as get_transcript), that chapters are free and cached, and implies async behavior via the wait_seconds parameter. This adds significant context beyond the annotations without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no fluff. The first sentence front-loads the core purpose and output structure; the second adds behavioral context. Every word earns its place, making it concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the output shape (chapters with start/end, title, summary) and the async/cost context, which covers the key information an agent needs. It does not explicitly mention the 'processing' status that wait_seconds can return, but that is covered in the schema. Given the lack of an output schema, the description provides adequate context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with all 5 parameters already documented in the schema. The description does not add any parameter-specific meaning beyond what the schema provides. Per the rubric, with high schema coverage the baseline is 3, and no additional value is added.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (split) and resource (public video into topic chapters) with details on output (start/end seconds, title, one-line summary). It also differentiates from get_transcript by noting it uses the transcript and has same cost/async behavior, clearly distinguishing its purpose from the sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives context by comparing to get_transcript (same cost and async behaviour) and notes that chapters are free/cached, which implies a good use case. However, it does not explicitly state when to use this instead of get_transcript or suggest_clips, nor any when-not conditions. The guidance is clear enough but lacks explicit exclusions.

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

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