yt-outlier-mcp

Get a video's replicable structure

get_video_structure

Fetch video structure including duration, chapters, tags, and transcript to analyze replicable formats of outlier videos.

Instructions

Fetch what makes an outlier video's format copyable: duration, chapters (parsed from the description), tags, and the transcript. Icon Method verification step: extract the structure, don't guess it. Costs 1 YouTube API quota unit (transcript is fetched outside the API at zero quota and may be unavailable for some videos).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "video": "<video>"
    },
    "name": "get_video_structure"
  }
}

Input Schema

Name	Required	Description
`video`	Yes	YouTube video ID or URL (watch/shorts/youtu.be forms)
`includeTranscript`	No	Fetch the transcript (slower; adds no quota cost)
`maxTranscriptChars`	No	Truncate the transcript to this many characters

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Jul 10, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations provided, the description effectively discloses cost (1 YouTube API quota unit), transcript fetching details (outside API, zero quota, may be unavailable), and potential slowness. However, it does not mention error handling, rate limits, or response format beyond listing returned fields.

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 description is concise, consisting of two sentences that front-load the purpose and then add critical cost/availability context. Every sentence is meaningful with no redundancy.

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?

Given the lack of an output schema and three optional parameters, the description adequately outlines the returned fields (duration, chapters, tags, transcript) and key constraints. It could be more explicit about the response structure, but the current text is sufficient for agent understanding.

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 covers 100% of parameters with descriptions, so the tool description adds minimal value for parameters. It does not elaborate on the video input format or the effects of includeTranscript/maxTranscriptChars, but the schema provides adequate defaults and types. Baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool fetches a video's structure (duration, chapters, tags, transcript) for replication. It's specific to outlier videos and the 'Icon Method,' but the jargon may confuse some agents. It distinguishes from sibling tools like find_outliers or get_comment_signal by focusing on structure.

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

Usage Guidelines2/5

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

The description lacks explicit guidance on when to use this tool versus alternatives. It mentions 'Icon Method verification step' but does not explain when not to use it or provide comparisons with siblings like find_outliers or search_niche_sweep.

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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