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veo_list_models

List all available Veo models with their features, supported actions, and image input rules to choose the right model for video generation.

Instructions

List all available Veo models and their capabilities.

Shows all available model versions with their features, supported actions,
and image input rules. Use this to understand which model to choose
for your video generation.

Model comparison:
- veo2/veo2-fast: Standard models, 1 image (first frame)
- veo3/veo3-fast: Improved quality, 1-3 images supported
- veo31/veo31-fast: Latest models, 1-3 images supported
- veo31-fast-ingredients: Multi-image fusion mode (ingredients2video action)

Returns:
    Table of all models with their capabilities and image rules.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the veo_list_models tool. Decorated with @mcp.tool(), it returns a formatted table string listing all available Veo models with their capabilities (text2video, image2video, image input rules), modes, aspect ratios, and resolutions.
    @mcp.tool()
    async def veo_list_models() -> str:
        """List all available Veo models and their capabilities.
    
        Shows all available model versions with their features, supported actions,
        and image input rules. Use this to understand which model to choose
        for your video generation.
    
        Model comparison:
        - veo2/veo2-fast: Standard models, 1 image (first frame)
        - veo3/veo3-fast: Improved quality, 1-3 images supported
        - veo31/veo31-fast: Latest models, 1-3 images supported
        - veo31-fast-ingredients: Multi-image fusion mode (ingredients2video action)
    
        Returns:
            Table of all models with their capabilities and image rules.
        """
        # Last updated: 2026-04-05
        return """Available Veo Models:
    
    | Model                  | Text2Video | Image2Video | Image Input Rules           |
    |------------------------|------------|-------------|------------------------------|
    | veo2                   | ✅         | ✅          | 1 image (first frame)        |
    | veo2-fast              | ✅         | ✅          | 1 image (first frame)        |
    | veo3                   | ✅         | ✅          | 1-3 images (first/last)      |
    | veo3-fast              | ✅         | ✅          | 1-3 images (first/last)      |
    | veo31                  | ✅         | ✅          | 1-3 images (first/last)      |
    | veo31-fast             | ✅         | ✅          | 1-3 images (first/last)      |
    | veo31-fast-ingredients  | ❌         | ✅          | 1-3 images (multi-fusion)    |
    
    Image Input Modes:
    - First Frame Mode (1 image): Video starts from your image
    - First/Last Frame Mode (2-3 images): Video interpolates between images
    - Multi-Fusion Mode (veo31-fast-ingredients only): Blends elements from all images
    
    Recommendations:
    - For quick generation: Use '-fast' suffix models
    - For best quality: Use veo31 or veo3 (non-fast)
    - For image fusion: Use veo31-fast-ingredients
    - For text-only: Any model except veo31-fast-ingredients
    
    Aspect Ratios:
    - 16:9: Landscape/widescreen (default)
    - 9:16: Portrait/vertical (social media stories)
    - 4:3: Standard
    - 3:4: Portrait standard
    - 1:1: Square
    
    Resolution Options:
    - 4k: Highest quality output
    - 1080p: Standard HD resolution
    - gif: Animated GIF format
    """
  • core/server.py:47-54 (registration)
    The FastMCP server instance (mcp) created as a FastMCP object. The @mcp.tool() decorator on veo_list_models in info_tools.py registers this tool with the MCP server.
    # Initialize FastMCP server
    mcp = FastMCP(
        settings.server_name,
        icons=[Icon(src="", mimeType="image/jpeg")],
        **mcp_kwargs,
    )
    
    logger.info(f"Initialized MCP server: {settings.server_name}")
  • main.py:176-176 (registration)
    The tool name 'veo_list_models' appears in the server card JSON response for HTTP transport, listing available tools.
    {"name": "veo_list_models", "description": "List available models"},
  • The VeoModel type definition (Literal type) that defines all valid model names referenced in the veo_list_models handler (veo2, veo2-fast, veo3, veo3-fast, veo31, veo31-fast, veo31-fast-ingredients).
    # Veo model versions
    VeoModel = Literal[
        "veo2",
        "veo2-fast",
        "veo3",
        "veo3-fast",
        "veo31",
        "veo31-fast",
        "veo31-fast-ingredients",
    ]
  • Integration test for veo_list_models - verifies the tool returns expected model names (veo2, veo3) when called.
    @pytest.mark.asyncio
    async def test_list_models(self) -> None:
        """Test veo_list_models tool."""
        from tools.info_tools import veo_list_models
    
        result = await veo_list_models()
    
        print("\n=== List Models Result ===")
        print(result)
    
        assert "veo2" in result
        assert "veo3" in result
Behavior4/5

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

No annotations exist, so the description carries full weight. It discloses the return value as a table of models with capabilities and image rules, which is sufficient for a read-only list operation. No side effects are expected, and it aligns with the tool's nature.

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 efficient: a two-sentence opening, a bulleted model comparison, and a return statement. Every sentence and bullet adds necessary information without redundancy. Front-loaded with purpose.

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?

Given zero parameters and the presence of an output schema, the description fully covers what the tool does and returns. It includes model version details and image rules, making it self-contained for an agent to decide to invoke it.

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

Parameters4/5

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

The input schema has zero parameters, so schema coverage is 100%. The description adds value by detailing the output (table of models with capabilities, supported actions, image input rules), which goes beyond the empty schema and helps the agent understand what to expect.

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 states 'List all available Veo models and their capabilities,' using a specific verb and resource. It distinguishes itself from sibling tools like veo_text_to_video by focusing on model discovery, not generation. The model comparison table further clarifies scope.

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 explicitly says 'Use this to understand which model to choose for your video generation,' providing a clear use case. It does not mention when not to use or alternatives to other list tools, but the context and sibling names imply this is for model 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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