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yt-analysis-mcp

yt-analysis-mcp

by Legorobotdude
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

summarize_video

Generate text summaries of YouTube videos at different detail levels to quickly understand content without watching the full video.

Instructions

Summarize a YouTube video's content. Returns a text summary based on the specified detail level.

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": {
      "youtube_url": "<youtube_url>"
    },
    "name": "summarize_video"
  }
}

Input Schema

NameRequiredDescriptionDefault
youtube_urlYesFull YouTube URL (youtube.com/watch?v=ID, youtu.be/ID, or youtube.com/shorts/ID)
detail_levelNoLevel of detail: brief (2-3 sentences), medium (key points with timestamps), detailed (comprehensive breakdown)medium

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description must cover behavioral aspects. It mentions it returns a text summary based on detail level, but does not disclose processing time, input validation, or error handling. The behavior is straightforward but minimally described.

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 two sentences: first states the core function, second explains output based on parameter. It is concise but could be more front-loaded about what the user gets. No fluff, but slightly vague on output format.

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

Completeness3/5

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

Given no output schema, the description should explain return format. It says 'returns a text summary' but not whether it's plain text, structured, or includes timestamps. With siblings like ask_about_video, more detail on output would help. Scores 3 as it covers basic purpose but lacks output specificity.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the detail_level enum values (brief, medium, detailed) with concrete expectations like 'key points with timestamps', which is not in the schema. This extra context earns a 4.

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 it summarizes a YouTube video's content and returns a text summary based on a detail level. It distinguishes from siblings like ask_about_video (querying) and extract_frames (visual extraction), but could be more explicit about the summary format.

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

Usage Guidelines3/5

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

No explicit when-to-use or alternatives are provided. The description implies usage when a text summary is needed, but does not clarify when to choose this over ask_about_video or other tools. However, the enum options for detail_level give some usage guidance.

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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