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YouTube Channel MCP

YouTube Channel MCP

by joinhackyard
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

SEO and packaging score

seo_check
Read-only

Score a video's title, description, tags, hashtags and chapters against YouTube best practices on a 0-100 scale, returning prioritized fixes. Runs without AI and works on drafts or published videos.

Instructions

Scores a video's (or draft's) title, description, tags, hashtags and chapters against YouTube best practice, 0-100 with fixes in order of impact. No AI needed; 1 quota unit for a video_id, none for a draft.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "seo_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNo
titleNo
channelNoWhich connected channel: channel ID (UC...), @handle or name. Omit to use the default channel.
keywordNoThe main search phrase you want to rank for.
video_idNo
descriptionNo
duration_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, openWorldHint), and the description adds real value beyond them: quota cost (1 unit with video_id, none for a draft), a 'no AI needed' note, and the return shape ('0-100 with fixes in order of impact'). It stops short of saying whether the call fails or degrades when required-looking inputs are absent.

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?

Two dense sentences with no filler, leading with what is scored and the output scale before the cost caveat. Every clause carries information an agent would otherwise have to guess.

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?

For a 7-parameter, zero-required, read-only scoring tool with no output schema, the definition covers purpose, output format and cost. The remaining gap is the all-optional schema: it never says what happens with an empty call or which combination of inputs is meaningful.

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 low (29%), so the description carries extra burden. It does map the scored fields to title/description/tags and explains the draft-vs-video_id mode distinction, but it names 'hashtags' and 'chapters' which are not actual parameters, and leaves keyword, duration_seconds and channel unaddressed.

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?

States a specific verb and resource: it 'scores a video's (or draft's) title, description, tags, hashtags and chapters against YouTube best practice'. An agent can tell this is an evaluation/audit tool rather than a generator like generate_metadata. It does not explicitly name a sibling it replaces, which keeps it from a 5.

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?

Usage is only implied: the video_id-vs-draft cost split hints at 'check before publishing' versus 'check an existing video', but there is no explicit when-to-use, when-not-to-use, or pointer to alternatives such as preview_metadata or analyze_video. The agent must infer the workflow.

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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