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get_most_replayed

Get the most replayed moments of a YouTube video, highlighting peaks where viewers rewatch most. Use these high-interest regions to jump to key parts or prioritize content for summaries.

Instructions

Get a YouTube video's "most replayed" moments -- the peaks of its viewer-interest heatmap (the curve shown above the timeline marking where people rewatch most).

Use this for "what are the best / most-rewatched parts?", "jump me to the good part", or to weight a summary toward what viewers actually care about. Each peak is a high-interest region (region_start_seconds..region_end_seconds) with the hottest instant at peak_start_seconds, a ready-to-share url that opens the video at the start of the stretch, and the chapter it falls in. relative_intensity is 0..1 within this video (1.0 = its single most-rewatched moment) -- it is NOT a view count and is not comparable across videos.

A peak with is_opening=True sits at the very start (t~=0): that spot is almost always inflated by playback starting there, not a genuine rewatch, so discount it as a "best part". It is returned in addition to (not counted against) top_n, so the opening can't crowd out content.

To say what is actually happening at a peak, read its peak_label (mm:ss) and look it up with get_transcript(include_timestamps=True); profile is a coarse 0..1 curve for the overall shape (front-loaded vs steady vs spikes near the end).

has_data may be False -- then peaks is empty and note explains why (many newer, low-traffic, or Shorts videos have no heatmap).

Args: video: A YouTube URL (watch, youtu.be, shorts, embed, live) or an 11-character video ID. top_n: Max number of content peak regions (clamped to 1..20; default 8). The flagged opening (t~=0) peak, when present, is returned in addition to these.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoYes
top_nNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_idYes
has_dataYes
duration_secondsYes
peaksYes
profileYes
noteYes
Behavior5/5

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

With no annotations, the description fully discloses key behaviors: relative_intensity is relative within video, not comparable; is_opening flag indicates inflated interest; has_data may be False; profile curve shape description. This provides comprehensive transparency.

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

Conciseness4/5

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

The description is detailed but well-structured, with main purpose upfront. Every sentence adds value, though some redundancy could be trimmed. Overall, it is appropriately sized for the complexity.

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 description covers all important aspects: output fields, edge cases (no heatmap, inflated opening), and usage guidance. Given the presence of an output schema, it does not need to detail return format, making the description complete.

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?

Although schema coverage is 0%, the description fully explains both parameters: video accepts various YouTube URL formats or video ID; top_n is clamped 1-20 with default 8 and the opening peak is additional. This compensates for the lack of schema 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 clearly states the tool retrieves 'most replayed' moments from a YouTube video, specifying the resource and the specific data. It distinguishes from siblings like get_transcript and get_video_metadata by focusing on viewer-interest heatmap peaks.

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 provides clear use cases (e.g., finding best parts, jumping to good parts) and warns about the inflated opening peak and the possibility of missing data. It does not explicitly exclude alternative tools, but the use cases are distinct enough from siblings.

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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