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x402vid AI Intelligence & Channel Growth for YouTube Videos

Thumbnail Concept

thumbnail_concept
Read-only

YouTube thumbnail concept: layout, overlay text, render prompt.

Reads the video's transcript, then specs a click-worthy thumbnail plus a ready-to-render image prompt. Text phase of thumbnail_creation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nicheNoOptional niche for style targeting
video_urlNoYouTube video URL or video ID
x_paymentNoBase64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context by explaining that the tool reads the video's transcript and outputs both a click-worthy thumbnail spec and a ready-to-render image prompt. This goes beyond the annotations and clarifies the tool's non-mutating, analysis-oriented behavior.

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 short, front-loaded with the tool's purpose, and every sentence earns its place. It states the output categories, the input source, and the pipeline relationship without fluff or repetition.

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 read-only, concept-generation tool with an output schema and well-covered parameter descriptions, the description is largely complete. It could be more explicit about needing video_url and the x_payment requirement, especially since the schema marks zero parameters as required, but the pipeline context and parameter descriptions compensate.

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 baseline is 3. The description adds little per-parameter meaning beyond the schema; it implies video_url is used to fetch the transcript, but it does not discuss niche or x_payment. The schema descriptions themselves carry the parameter semantics.

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 identifies the tool's job: read a video transcript and produce a thumbnail concept with layout, overlay text, and a render prompt. It also explicitly frames itself as the 'text phase of thumbnail_creation,' which distinguishes it from the sibling tool thumbnail_creation.

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 clear context by positioning this tool as the text phase of thumbnail_creation, so an agent can infer it should be used before the rendering/full thumbnail step. It does not explicitly state when-not-to-use or name other alternatives, but the pipeline framing is strong enough for correct selection.

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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