MnogoReels MCP server

Create YouTube thumbnails

create_youtube_thumbnails

Generate 1-4 AI YouTube thumbnail JPEGs from a video link, using source frames with optional title, language, art direction, and style.

Instructions

Generates 1 to 4 AI YouTube thumbnail images (1280×720 JPEG) for a video, using frames from the video plus an optional title and art direction. Free: spends no credits, but every variant is a separate AI image generation, so ask for more than one only when the user wants options.

Parameters: source_url (link to the video), language of the text on the thumbnail (required; match the title or the video), title (text to place on the thumbnail, up to 120 characters), prompt (art direction, up to 1000 characters), style (auto, bold, cinematic, minimal, news, gaming) and variants (1 to 4, default 1).

Returns thumbnail_job_id right away. It usually finishes in 1 to 2 minutes. Next, call get_youtube_thumbnails with wait_sec=50.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`style`	No	auto, bold, cinematic, minimal, news or gaming.	auto
`title`	No	Text printed on the thumbnail, ≤120 characters; omit to let the AI write it.
`prompt`	No	Art direction, e.g. "surprised face, red arrow, dark background".
`language`	Yes	Two-letter language of the text on the thumbnail, e.g. en; match the title or the video.
`variants`	No	Number of images, 1–4 (each is a separate AI generation).
`source_url`	Yes	Public link to the video: YouTube, VK Video, RuTube, Dzen, Twitch, Yandex Disk file, Google Drive file, or a direct .mp4/.mov/.mkv/.webm link. Local files are not supported.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`next_step`	Yes
`thumbnail_job_id`	Yes
`variants_requested`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 15, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations only cover safety profile; the description adds the async contract (returns thumbnail_job_id immediately, finishes in 1-2 minutes), the cost model (free, no credits, but each variant is a separate generation), and the required follow-up call. This is exactly the context annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads purpose, then a behavior note, then parameters, then the async flow. Slight redundancy between the parameter list in prose and the schema's own descriptions costs it a point, but overall tight and well ordered.

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

Completeness5/5

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

An output schema exists, so return values need not be detailed; the description still supplies the essential async lifecycle, cost behavior and the follow-up tool. Everything an agent needs to invoke and then poll correctly is present.

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 schema already documents types, enums, defaults and length limits. The description reinforces the required params (source_url, language) and mentions constraints, but adds no syntax or semantics beyond the schema. Baseline 3 is correct.

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?

States a specific verb and resource ('Generates 1 to 4 AI YouTube thumbnail images') with resolution, format and inputs, and names the companion retrieval tool get_youtube_thumbnails. An agent can distinguish it from siblings like create_clip_job or create_article without opening schemas.

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?

Explains when to request multiple variants ('ask for more than one only when the user wants options') and gives the explicit follow-up call pattern with wait_sec=50. It lacks explicit when-not-to-use framing against other image/clip tools, so not a perfect 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.