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summarize_chapters

Break a YouTube transcript into time-blocked chunks at natural pauses, then summarize each section individually for easier review.

Instructions

Break a transcript into rough time-blocked chunks at natural pauses, for easier per-section summarizing.

This is a heuristic, not real chapter data: it splits wherever the gap between two consecutive
caption snippets exceeds `gap_seconds`, then merges any resulting chunk shorter than
`min_chunk_seconds` into its neighbor so chunks stay summarizable.

Args:
    video_url_or_id: Full YouTube URL or bare 11-char video ID.
    gap_seconds: Silence gap (seconds) between captions that triggers a new chunk boundary.
    min_chunk_seconds: Minimum chunk duration; shorter chunks get merged forward.
    languages: Preferred language codes in priority order. Defaults to ["en"].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languagesNo
gap_secondsNo
video_url_or_idYes
min_chunk_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so admirably. It explicitly discloses that this is 'a heuristic, not real chapter data' and explains the exact split/merge algorithm involving gap_seconds and min_chunk_seconds, so the agent knows what behavior to expect.

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?

The definition is front-loaded with a one-sentence summary, followed by a concise explanation of the heuristic behavior and a clean Args section. Every sentence carries necessary information, and there is no filler.

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

Completeness5/5

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

The tool has an output schema, so return values need not be repeated. The description covers the algorithm, parameters, defaults, and limitations, which is everything an agent needs to call it correctly. The only minor nuance is languages defaulting to ['en'] in the prose while the schema shows null, but the behavior is still clear.

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

Parameters5/5

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

Schema description coverage is 0%, so the description alone documents semantics. It gives meaningful explanations for all four parameters, including units ('seconds'), behavior ('triggers a new chunk boundary', 'merged forward'), and accepted values for video_url_or_id. This fully compensates for the schema's lack of descriptions.

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 opens with a specific verb and resource: 'Break a transcript into rough time-blocked chunks at natural pauses', which clearly defines the tool's output and purpose. It further distinguishes itself from real chapter data and from the sibling retrieval/search tools by stating it is a heuristic chunking operation.

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 phrase 'for easier per-section summarizing' gives a clear context and intended use. It does not explicitly name alternatives (get_transcript, search_transcript) or state when not to use it, so it stops short of a 5, but it is not ambiguous.

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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