yt-outlier-mcp

Read a video's comment demand signal

get_comment_signal

Retrieve a video's top comments and metrics on questions and demand phrasing to confirm unmet audience demand.

Instructions

Fetch a video's top comments (relevance-ordered) plus simple demand metrics: how many ask questions and how many use demand phrasing ('please make...', 'part 2', 'how do you...'). Icon Method verification step: comments prove the topic has unmet demand, not just views. Costs 1 YouTube API quota unit.

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": "<video>"
    },
    "name": "get_comment_signal"
  }
}

Input Schema

Name	Required	Description	Default
`video`	Yes	YouTube video ID or URL (watch/shorts/youtu.be forms)
`maxComments`	No	Number of top comments to fetch

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Jul 10, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries full burden. It discloses that comments are relevance-ordered, metrics are simple (questions and demand phrasing), and costs 1 quota unit. It does not explicitly state whether the tool is read-only or any authentication needs, but these are implied.

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 two sentences plus a cost note, highly concise and front-loaded. Every sentence adds value: purpose, use case, and cost.

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

Completeness4/5

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

Given 2 parameters, no output schema, and no annotations, the description explains the tool's output (top comments and demand metrics) and purpose (verification step). It could describe the return structure more explicitly but is adequate for selection.

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 coverage is 100%, so baseline is 3. The description adds minimal new meaning beyond the schema: it repeats that 'video' can be ID or URL and mentions default values for maxComments. No additional semantic depth.

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 clearly states the verb 'Fetch' and the resource 'video's top comments plus simple demand metrics'. It distinguishes itself from sibling tools like find_outliers, get_video_structure, and search_niche_sweep by focusing on comment-based demand signal.

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

Usage Guidelines4/5

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

The description provides a specific use case: 'Icon Method verification step: comments prove the topic has unmet demand, not just views.' It also notes the cost in API quota. However, it does not explicitly state when not to use this tool or mention alternatives.

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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