YT-NINJA

generate_chapters

Create structured chapter markers for YouTube videos to improve navigation and content organization.

Instructions

Generate chapter markers for video

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": {
      "url": "<url>"
    },
    "name": "generate_chapters"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	YouTube video URL

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 11, 2026

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 what the tool does but doesn't explain how it works—such as whether it analyzes video content, transcripts, or metadata; what format the chapter markers are in; or if there are limitations like video length or processing time. This leaves significant gaps in understanding the tool's behavior.

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, clear sentence with no wasted words, making it highly concise and front-loaded. It directly communicates the core function without unnecessary elaboration, earning a perfect score for efficiency.

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 tool's complexity (involving video analysis and chapter generation) and lack of annotations or output schema, the description is insufficient. It doesn't explain what the output looks like (e.g., timestamps, titles), any prerequisites, or error handling, leaving the agent with incomplete context for effective use.

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 'url' parameter documented as 'YouTube video URL'. The description doesn't add any meaning beyond this, such as clarifying URL formats or restrictions. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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 'Generate chapter markers for video' clearly states the action (generate) and resource (chapter markers for video), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'generate_video_highlights' or 'summarize_video' which might have overlapping video analysis functions, preventing a perfect score.

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. With siblings like 'generate_video_highlights' and 'summarize_video' that might serve similar video content analysis purposes, there's no indication of when chapter generation is preferred over other methods, leaving usage 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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