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MCP YouTube Intelligence

MCP YouTube Intelligence

by JangHyuckYun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

segment_topics

Segment YouTube video transcripts into distinct topics using transition markers to organize content for analysis and reporting.

Instructions

Segment a video transcript into topics based on transition markers.

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_id": "<video_id>"
    },
    "name": "segment_topics"
  }
}

Input Schema

NameRequiredDescriptionDefault
video_idYesYouTube video ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool segments transcripts but doesn't describe what 'transition markers' are, how topics are defined, the output format (e.g., list of segments with timestamps), error handling, or any rate limits. This leaves significant gaps for a tool that performs analysis on video content.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by conveying essential information.

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

Completeness2/5

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

Given the complexity of segmenting video transcripts (an analysis task) with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context, which are critical for an AI agent to invoke it correctly. The high schema coverage doesn't compensate for these gaps in a non-trivial tool.

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 input schema has 100% description coverage, with the single parameter 'video_id' documented as 'YouTube video ID.' The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 action ('segment') and resource ('video transcript') with the specific purpose of dividing it 'into topics based on transition markers.' It distinguishes from siblings like 'get_transcript' (retrieval) or 'search_transcripts' (searching), but doesn't explicitly contrast with all alternatives. The purpose is specific but not fully differentiated from all siblings.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a transcript first), exclusions, or compare to siblings like 'extract_entities' or 'search_transcripts' for similar text analysis tasks. Usage is implied by the purpose but lacks explicit context.

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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