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AceDataCloud

SeedreamMCP

by AceDataCloud
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

seedream_list_sizes

List all available image sizes and resolution options to help you choose the right size for your project.

Instructions

List all available image sizes and resolution options for Seedream.

Use this when:
- User asks about available image sizes
- You need to help choose the right resolution
- You want to understand size options

Returns:
    Formatted list of all size options with descriptions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • tools/info_tools.py:72-112 (handler)
    The actual handler function for the seedream_list_sizes tool. It is an async function decorated with @mcp.tool() that returns a formatted string listing all available image sizes (1K, 2K, 3K, 4K, adaptive) and custom dimensions.
    @mcp.tool()
async def seedream_list_sizes() -> str:
    """List all available image sizes and resolution options for Seedream.

    Use this when:
    - User asks about available image sizes
    - You need to help choose the right resolution
    - You want to understand size options

    Returns:
        Formatted list of all size options with descriptions.
    """
    # Last updated: 2026-04-05
    return """# Seedream Image Size Options

## Preset Sizes

| Size | Description | Best For |
|------|-------------|----------|
| `1K` | ~1024px (default) | General use, fast generation |
| `2K` | ~2048px | Higher detail, print-ready |
| `3K` | ~3072px | High detail, large prints |
| `4K` | ~4096px | Maximum quality, large prints |
| `adaptive` | Auto-selected based on content | Let the model choose optimal size |

## Custom Dimensions

You can also specify exact dimensions in `WIDTHxHEIGHT` format:
- `1024x1024` — Square (1:1)
- `1280x720` — Landscape (16:9)
- `720x1280` — Portrait (9:16)
- `1024x768` — Landscape (4:3)
- `768x1024` — Portrait (3:4)

## Tips
- **1K** is fastest and most cost-effective
- **4K** provides stunning detail but takes longer
- **adaptive** is great when you're unsure about the best size
- Custom dimensions give full control over aspect ratio
- All models support all size options
"""
  • tools/info_tools.py:6-7 (registration)
    The @mcp.tool() decorator registers seedream_list_sizes as an MCP tool on the FastMCP server instance.
    @mcp.tool()
async def seedream_list_models() -> str:
  • main.py:178-181 (registration)
    Explicit listing of seedream_list_sizes in the server card JSON response for HTTP transport mode.
    {
    "name": "seedream_list_sizes",
    "description": "List supported image sizes",
},
  • main.py:123-123 (registration)
    The tool name is printed in the startup banner as an available tool.
    safe_print("    - seedream_list_sizes")
  • prompts/__init__.py:66-69 (helper)
    Reference to seedream_list_sizes in the image generation guide prompt, describing when to use this tool.
    ## Size Information
**Tool:** `seedream_list_sizes`
**Use when:**
- User asks about available image sizes or resolutions

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description must disclose behavior. It states that it returns a formatted list of size options, indicating a read-only operation. While it doesn't explicitly mention safety or side effects, the nature of the tool (listing) implies no destructive actions.

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 concise, with only three lines of usage guidance and one line for return value. It is front-loaded, no fluff, and every sentence is meaningful.

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 no parameters and an existing output schema, the description is complete. It covers when to use the tool and what to expect as output, making it self-sufficient.

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 the description does not need to add parameter info. Per the guidelines, '0 params = baseline 4'.

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's function with specific verb 'List' and resource 'image sizes and resolution options for Seedream'. It distinguishes itself from sibling tools like seedream_generate_image or seedream_edit_image.

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?

It provides explicit scenarios for when to use the tool, such as when the user asks about available sizes or needs to choose a resolution. It does not mention when not to use it, but the guidance is clear and sufficient for a simple listing tool.

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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