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MCP YouTube Intelligence

MCP YouTube Intelligence

by JangHyuckYun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

generate_report

Create structured markdown reports for YouTube videos with summaries, topic segments, entity extraction, and optional comment analysis.

Instructions

Generate a structured markdown report for a YouTube video. Includes summary, topic segments, entities, and optionally comments.

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": "generate_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
video_idYesYouTube video ID
include_commentsNoInclude comment analysis
llm_providerNoLLM provider for summary

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

B3.2/5.0
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. It mentions the report includes summary, topic segments, entities, and optionally comments, but lacks details on behavioral traits like processing time, error handling, authentication needs, or rate limits. For a tool with no annotations, this is a significant gap in 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 a single, efficient sentence that front-loads the main purpose and lists key components. It avoids unnecessary details, though it could be slightly more structured for clarity.

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 annotations, no output schema, and 3 parameters with full schema coverage, the description is adequate but incomplete. It covers the purpose and components but lacks behavioral context and output details, making it minimally viable for a report-generation 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?

Schema description coverage is 100%, so the schema already documents all parameters (video_id, include_comments, llm_provider). The description adds minimal value by implying the report uses an LLM for summary and includes comment analysis, but doesn't provide additional syntax or format details beyond what the schema provides.

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 verb 'generate' and the resource 'structured markdown report for a YouTube video', specifying the content includes summary, topic segments, entities, and optionally comments. It distinguishes from siblings like get_transcript or extract_entities by focusing on report generation, though it doesn't explicitly contrast with all alternatives.

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?

The description implies usage when a comprehensive report is needed, mentioning optional comment inclusion, but doesn't specify when to use this versus siblings like get_video or segment_topics individually. No explicit alternatives or exclusions are provided, leaving some ambiguity.

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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