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el-el-san

AI Video Generator MCP Server

by el-el-san

generate-video

Create videos from text prompts and images using AI models, with options to control aspect ratio, resolution, duration, and looping for customized outputs.

Instructions

Generate a video from text prompt and/or images using AI models (Luma or Kling)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aspect_ratioNoAspect ratio of the video16:9
durationNoDuration of the video (9s costs 2x more)5s
end_image_urlNoFinal image to end the video with (URL or base64 data URI)
image_urlNoInitial image to start the video from (URL or base64 data URI)
loopNoWhether the video should loop (blend end with beginning)
modelNoAI model to use (luma=Ray2, kling=Kling)luma
promptYesText description of the desired video content
resolutionNoResolution of the video (higher resolutions use more credits)540p
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions AI models but doesn't disclose important behavioral traits like: whether this is a synchronous or asynchronous operation, what permissions or authentication are needed, rate limits, credit costs (beyond the hint in the duration parameter schema), or what the output looks like. The description is minimal and lacks crucial operational context.

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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information. Every word earns its place in this concise formulation.

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

Completeness2/5

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

For a complex video generation tool with 8 parameters and no annotations or output schema, the description is insufficient. It doesn't explain the operation's nature (async/sync), authentication requirements, cost implications beyond the duration hint, error conditions, or what happens after invocation. The combination of complexity and lack of structured metadata demands more comprehensive description.

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 all 8 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone, but the description doesn't enhance understanding of parameter usage or relationships.

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?

The description clearly states the tool's purpose: 'Generate a video from text prompt and/or images using AI models (Luma or Kling)'. It specifies the verb ('generate'), resource ('video'), and input sources ('text prompt and/or images'), but doesn't differentiate from its sibling tool 'check-video-status' beyond the obvious generation vs. status check distinction.

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?

The description implies usage context by mentioning AI models (Luma or Kling), suggesting this is for AI-generated video creation. However, it doesn't provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. The sibling tool 'check-video-status' is clearly complementary rather than an alternative.

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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