media-mcp

generate_video

Generate videos from text prompts or reference images, with support for video extension and frame-specified generation via Veo models.

Instructions

Generate videos from text prompts or reference images using Google's Veo models.

Supports text-to-video, image-to-video, video extension, and frame-specified generation. Generation is asynchronous.

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": {
      "prompt": "<prompt>"
    },
    "name": "generate_video"
  }
}

Input Schema

Name	Required	Default
`model`	No	veo-3.1
`prompt`	Yes
`resolution`	No
`aspect_ratio`	No	16:9
`extend_video_id`	No
`last_frame_image`	No
`reference_images`	No
`first_frame_image`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.7Jul 29, 2026

TDQS

B3.3/5.0

Behavior3/5

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

Discloses that generation is asynchronous, which is a key behavioral trait. However, with no annotations, the description does not cover other aspects like auth needs, rate limits, or return format, leaving gaps in transparency.

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?

Three concise sentences front-loading key information about purpose, capabilities, and async nature. No wasted words.

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?

Missing crucial details for an async tool, such as how to retrieve generated videos, polling mechanism, or expected output format. With no output schema and 8 parameters, the description is insufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain the purpose of parameters like extend_video_id, reference_images, etc. It only lists modes without mapping them to specific parameters, providing minimal additive value.

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?

Clearly states that the tool generates videos from text prompts or reference images using Google's Veo models, and lists specific modes (text-to-video, image-to-video, etc.), distinguishing it from sibling tools like generate_image.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Does not provide explicit guidance on when to use this tool vs. alternatives (generate_image, generate_speech, generate_music). The description implies usage for video generation but lacks when-not-to-use or prerequisites.

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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