ModelsLab v8

Video Generation (V8)

video-generation

Generate videos using the ModelsLab V8 API.
Supports any video endpoint (text-to-video, image-to-video, video-to-video, lip-sync, motion-control, etc.).
Pass the `endpoint` slug (e.g. "text-to-video") and all required parameters for that endpoint.
Returns a request ID that can be used with the fetch-generation tool to retrieve results.
Use the list-models tool to find available model IDs.

Input Schema

TableJSON Schema

Name	Required	Description
`params`	No	Additional parameters for the endpoint (e.g. prompt, init_image, init_video, init_audio, duration, aspect_ratio, webhook, track_id).
`endpoint`	Yes	The video operation slug.
`model_id`	Yes	The model ID to use for video generation.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 2, 2026

TDQS

A4.1/5.0

Behavior4/5

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

The description discloses the key behavioral trait that the call is asynchronous, returning a request ID that must later be redeemed via fetch-generation. Annotations only declare openWorldHint, so this workflow disclosure carries real value. It does not cover auth, cost, or rate-limit behavior.

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?

Five short sentences, front-loaded with the core action and followed by the mechanics. Each sentence adds information, though it is slightly more verbose than strictly necessary.

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 compensates by explaining that the return is a request ID redeemable via fetch-generation. Combined with the endpoint guidance, this is nearly complete for a flexible generation tool.

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 the parameters are already documented. The description adds example endpoint slugs ('text-to-video'), which is useful since the endpoint enum is empty in the schema, but it otherwise repeats what the schema provides.

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+resource ('Generate videos') and scopes it as a general-purpose generator spanning text-to-video, image-to-video, video-to-video, lip-sync, and motion-control. This clearly separates it from siblings like image-generation and audio-generation.

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?

Explicitly routes the agent to list-models for model IDs and fetch-generation for results, giving clear context for the call. It stops short of stating when not to use this tool or which endpoint slug suits which scenario.

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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