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Glama
ECNU3D

Universal Image Generator MCP Server

by ECNU3D
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
GOOGLE_MODELNoModel to use with Google provider (only for Google provider, defaults to 'gemini')gemini
ZHIPU_API_KEYNoAPI key for ZhipuAI
GEMINI_API_KEYNoAPI key for Google (Gemini/Imagen)
IMAGE_PROVIDERYesChoose between Google (Imagen/Gemini), ZhipuAI, or Bailian
DASHSCOPE_API_KEYNoAPI key for Alibaba Bailian
OUTPUT_IMAGE_PATHNoDirectory to save generated images (optional)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
generate_image_from_textA

Generate an image based on the given text prompt using the configured image provider.

Args:
    prompt: User's text prompt describing the desired image to generate
    model_type: Optional model type for Google provider ("gemini" or "imagen"). 
               If not specified, uses the default from GOOGLE_MODEL env var.
    
Returns:
    Path to the generated image file using the configured provider's image generation capabilities
transform_image_from_encodedA

Transform an existing image based on the given text prompt using the configured image provider.

    Args:
        encoded_image: Base64 encoded image data with header. Must be in format:
                    "data:image/[format];base64,[data]"
                    Where [format] can be: png, jpeg, jpg, gif, webp, etc.
        prompt: Text prompt describing the desired transformation or modifications
        
    Returns:
        Path to the transformed image file saved on the server
transform_image_from_urlB

Transform an existing image from a URL using the configured image provider.

    Args:
        image_url: Remote or Public URL of the image to be transformed
        prompt: Text prompt describing the desired transformation or modifications
        function: WanX editing function (default: 'description_edit'). Supported functions:
                 'description_edit', 'description_edit_with_mask', 'stylization_all', 
                 'stylization_local', 'remove_watermark', 'expand', 'super_resolution', 
                 'colorization', 'doodle', 'control_cartoon_feature'
        mask_image_url: URL of mask image (required for 'description_edit_with_mask')
        
    Returns:
        Details about the transformed image including local path and remote URL
transform_image_from_fileA

Transform an existing image file based on the given text prompt using the configured image provider.

    Args:
        image_file_path: Path to the image file to be transformed
        prompt: Text prompt describing the desired transformation or modifications
        
    Returns:
        Path to the transformed image file saved on the server

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no ambiguity. 'generate_image_from_text' creates new images from scratch, while the three 'transform_image_from_*' tools all modify existing images but differ in their input sources (encoded data, local file, URL). The descriptions clearly differentiate these input methods, preventing misselection.

Naming Consistency5/5

All four tools follow a perfect verb_object_from_source pattern: 'generate_image_from_text', 'transform_image_from_encoded', 'transform_image_from_file', and 'transform_image_from_url'. This consistent naming convention makes the tool purposes immediately understandable and predictable.

Tool Count4/5

Four tools is reasonable for an image generation/transformation server, though slightly minimal. The set covers core functionality well, but could potentially benefit from additional utilities like image analysis or format conversion tools. The count is appropriate for the basic scope presented.

Completeness4/5

The tool surface covers the essential workflows for image generation and transformation comprehensively. It provides multiple input methods for transformations (encoded, file, URL) which is thorough. A minor gap exists in not having a dedicated tool for pure image analysis or metadata extraction, but the core functionality is well-covered.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources