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sora_list_models

List available Sora models with their capabilities, limits, and recommended use cases to select the right model for video generation.

Instructions

List all available Sora models and their capabilities.

Shows all available model versions with their limits, features, and
recommended use cases. Use this to understand which model to choose
for your video generation.

Returns:
    Table of all models with their version, limits, and features.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The actual handler function for the sora_list_models tool. Decorated with @mcp.tool() and returns a hardcoded table string of available Sora models with their capabilities.
    async def sora_list_models() -> str:
        """List all available Sora models and their capabilities.
    
        Shows all available model versions with their limits, features, and
        recommended use cases. Use this to understand which model to choose
        for your video generation.
    
        Returns:
            Table of all models with their version, limits, and features.
        """
        # Last updated: 2026-04-05
        return """Available Sora Models:
    
    | Model        | Max Duration (v1) | Max Duration (v2) | Quality | Features                          |
    |--------------|--------------------|--------------------|---------|-----------------------------------|
    | sora-2       | 15 seconds         | 12 seconds         | Good    | Standard generation, cost-effective |
    | sora-2-pro   | 25 seconds         | 12 seconds         | Best    | Longer videos, higher quality     |
    
    === Version 1 Options ===
    
    Video Size:
    | Size   | Description                    |
    |--------|--------------------------------|
    | small  | Standard definition            |
    | large  | HD (only sora-2-pro for 25s)   |
    
    Video Orientation:
    | Orientation | Aspect Ratio | Use Case                    |
    |-------------|--------------|-----------------------------|
    | landscape   | 16:9         | YouTube, presentations      |
    | portrait    | 9:16         | TikTok, Instagram Stories   |
    
    Duration:
    | Duration | Models                    |
    |----------|---------------------------|
    | 10s      | sora-2, sora-2-pro       |
    | 15s      | sora-2, sora-2-pro       |
    | 25s      | sora-2-pro only          |
    
    === Version 2 Options ===
    
    Video Resolution:
    | Resolution | Orientation | Description              |
    |------------|-------------|--------------------------|
    | 720x1280   | Vertical    | HD vertical (default)    |
    | 1280x720   | Horizontal  | HD horizontal            |
    | 1024x1792   | Vertical    | Tall vertical            |
    | 1792x1024   | Horizontal  | Wide horizontal          |
    
    Duration (seconds):
    | Seconds | Models                    |
    |---------|---------------------------|
    | 4s      | sora-2, sora-2-pro       |
    | 8s      | sora-2, sora-2-pro       |
    | 12s     | sora-2, sora-2-pro       |
    
    Recommendations:
    - Version 1: Use 'sora-2' with 'large' size for most use cases. Use 'sora-2-pro' for 25s videos.
    - Version 2: Use for precise resolution control and shorter videos (4-12s). Supports image reference via input_reference.
    """
  • The mcp server instance is imported from core.server, and the @mcp.tool() decorator on the handler registers it as an MCP tool.
    from core.server import mcp
  • tools/__init__.py:4-10 (registration)
    Imports info_tools module to trigger registration of all info tools (including sora_list_models) with the MCP server.
    from tools import info_tools, task_tools, video_tools
    
    __all__ = [
        "video_tools",
        "task_tools",
        "info_tools",
    ]
  • main.py:132-133 (registration)
    Startup banner listing sora_list_models as an available tool.
    safe_print("    - sora_list_models")
    safe_print("    - sora_list_actions")
  • main.py:189-189 (registration)
    Server card registration listing sora_list_models in the HTTP endpoint's tool manifest.
    {"name": "sora_list_models", "description": "List available models"},
Behavior3/5

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

No annotations are present, so the description carries full responsibility. It accurately describes the tool as a read-only list operation returning a table of models. While it does not disclose side effects or authorization needs (not applicable), it adds minimal behavioral context beyond the obvious. The description is adequate but not rich.

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?

The description is concise at four sentences, front-loading the key action 'List all available Sora models.' It avoids unnecessary detail and is well-structured for quick comprehension.

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?

Given the simplicity of the list operation and the presence of an output schema, the description adequately covers the purpose, return format, and usage context. It is complete enough for an agent to decide when and how to use this tool.

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 tool has zero parameters, and the input schema is empty with 100% description coverage. The description does not need to explain parameters. Per guidelines, baseline is 4 for 0 parameters.

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 the tool lists all available Sora models with their capabilities, limits, and features. It distinguishes itself from sibling generation tools by focusing on querying model information rather than generating content.

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 clear context for when to use it. However, it does not mention when not to use it or suggest alternatives, but given the sibling tools are different in purpose, the guidance is sufficient.

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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