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YouTube Channel MCP

YouTube Channel MCP

by joinhackyard
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Design and generate thumbnails

generate_thumbnails

Creates thumbnail concepts from a title and context, then renders 1280x720 JPGs under 2 MB for YouTube uploads or A/B tests; optionally builds on a reference video frame.

Instructions

Designs thumbnail concepts for a title (text overlay + image prompt) with the text model, then renders them with the image model (OpenAI gpt-image, Azure OpenAI image deployments, Gemini image). Optionally builds on a video frame. Saves 1280x720 JPGs under 2 MB, ready for set_thumbnail or create_ab_test. Without an image model it returns the concepts only.

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": {
      "title": "<title>"
    },
    "name": "generate_thumbnails"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNo
styleNoe.g. "bold, high contrast, face on the left, dark background"
titleYes
contextNoWhat the video is about, the subject, the audience.
reference_imageNoPath to a frame or photo to build on.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already flag readOnlyHint=false and openWorldHint=true, and the description adds real context beyond them: it explains the two-model pipeline and names the external image providers, which justifies the open-world hint. It also discloses concrete artifact constraints (1280x720 JPG, under 2 MB) and the no-image-model fallback. Gaps remain on where files are written, cost/latency, and failure modes.

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?

Four tight sentences, front-loaded with the core action, then capability details, then output constraints, then the degradation case. Nothing is redundant; the vendor list is the only mildly expendable detail.

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?

With no output schema, the description carries the return-shape burden and mostly does: it states that JPGs are saved and usable by set_thumbnail, and what comes back in the fallback case. It still does not describe the normal success payload (paths, URLs, concept text) that an agent must parse to chain calls.

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 60%, so the schema covers style and context on its own. The description adds meaning for reference_image ("build on a video frame") and title's role in concept generation, but says nothing about the count parameter's range or default. This is a baseline-3 case where the schema does most of the work.

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 two-stage verb pair (design concepts, then render) on a specific resource (thumbnails), and it explicitly routes output to set_thumbnail or create_ab_test, which distinguishes it from those siblings. An agent can tell this is a generator, not an uploader or critic.

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?

Useful conditions are stated: it can optionally build on a video frame, and without an image model it degrades to concepts only. The downstream hand-off to set_thumbnail / create_ab_test clarifies the workflow position. However, it never mentions how it relates to critique_thumbnails or when to prefer pre-made artwork.

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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