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MCP YouTube Intelligence

MCP YouTube Intelligence

by JangHyuckYun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_video

Extract video metadata and generate concise summaries from YouTube videos using video ID input for analysis and reporting.

Instructions

Get video metadata + summary (~300 tokens). Provide a YouTube video ID.

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": "get_video"
  }
}

Input Schema

NameRequiredDescriptionDefault
video_idYesYouTube video ID (e.g. dQw4w9WgXcQ)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the output includes 'metadata + summary (~300 tokens)', which gives some behavioral context about the response format and length. However, it doesn't address important aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions.

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 extremely concise with just two sentences that each serve a clear purpose: first states what the tool does, second specifies the required input. There's zero wasted language or redundancy.

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?

For a single-parameter read operation with no output schema, the description provides basic functionality but lacks important context. It doesn't explain what specific metadata fields are returned, how the summary is generated, or any limitations. The ~300 token mention is helpful but insufficient for full understanding of the tool's behavior.

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 the single parameter completely. The description adds minimal value beyond the schema by specifying 'YouTube video ID' (implied in schema's example) and reinforcing it's required. Baseline 3 is appropriate when 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 tool's purpose: 'Get video metadata + summary (~300 tokens)' with the specific resource being a YouTube video. It distinguishes from siblings like get_transcript (which gets transcript text) and get_comments (which gets comments), but doesn't explicitly mention these distinctions.

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 minimal usage guidance: 'Provide a YouTube video ID' indicates the required input but offers no context about when to use this tool versus alternatives like get_transcript or search_youtube. There's no mention of prerequisites, limitations, or comparative use cases.

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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