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YouTube DLP MCP Server

YouTube DLP MCP Server

by AgentX-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get-top-comments

Extract the most-liked top comments from any YouTube video by providing its URL, with optional count up to 20.

Instructions

Extract top comments from a YouTube video (sorted by likes)

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": "get-top-comments"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesYouTube video URL to extract comments from
countNoNumber of top comments to extract (default: 10, maximum: 20)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It adds meaningful behavior by stating comments are sorted by likes, but it does not mention the return format, pagination, failure cases, or that it only returns top comments rather than all comments.

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 one efficient sentence with no filler. The core action, resource, and sorting behavior are all front-loaded, making it easy for an agent to scan and understand quickly.

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?

The tool is simple and the description plus schema cover the required URL and optional count. Still, there is no output schema and no annotation context, so the agent is left without explicit indication of what the response shape looks like or which sibling to prefer in related video requests.

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%, with both 'url' and 'count' already described including defaults and bounds. The description's 'sorted by likes' further clarifies what 'top' means but does not add meaningful parameter-level semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Extract'), a clear resource ('top comments from a YouTube video'), and a distinguishing qualifier ('sorted by likes'). It is immediately distinguishable from siblings like get-video-info and get-video-subtitles without needing to inspect their schemas.

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 when to use the tool: when the agent needs the most-liked comments on a YouTube video. However, it gives no explicit guidance about when not to use it or which sibling should be selected instead for metadata or transcription needs.

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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