universal-image-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AWS_REGION | No | AWS region (default: us-east-1) | us-east-1 |
| ENABLE_AWS | No | Enable AWS Bedrock provider (true/false, default: false) | false |
| AWS_PROFILE | No | AWS profile name (default, SSO, or named profile) | |
| ENABLE_GEMINI | No | Enable Google Gemini provider (true/false, default: false) | false |
| ENABLE_OPENAI | No | Enable OpenAI provider (true/false, default: false) | false |
| GEMINI_API_KEY | No | Google Gemini API key (required if ENABLE_GEMINI is true) | |
| OPENAI_API_KEY | No | OpenAI API key (required if ENABLE_OPENAI is true) |
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_modelsA | List available image generation models from all enabled providers. Returns a formatted list of model IDs that can be used with generate_image and transform_image. Models are fetched dynamically from each provider's API. |
| generate_imageA | Generate an image from a text prompt using the specified model. Args: prompt: Detailed text description of the image to generate. Be specific about subject, style, lighting, colors, composition, and mood. Example: "A fluffy orange cat sitting on a windowsill, golden hour lighting, watercolor style" model_id: Model identifier from list_models(). Examples: "amazon.nova-canvas-v1:0", "gpt-image-1.5", "models/gemini-2.5-flash-image" output_path: Absolute or relative file path where the generated image will be saved. Supports PNG, JPEG formats. Parent directories are created automatically. reference_image: Optional. Path to an existing image to use as style/content reference. The model will generate a new image influenced by this reference. width: Optional. Image width in pixels. Default: 1024. Common values: 512, 768, 1024, 1280. Note: Some models only support specific sizes. Max: 4096px. height: Optional. Image height in pixels. Default: 1024. Common values: 512, 768, 1024, 1280. Note: Some models only support specific sizes. Max: 4096px. Returns: Success message with output path, or error description. |
| transform_imageA | Transform an existing image based on a text prompt. Args: image_path: Path to the source image to transform. Supports common formats (PNG, JPEG, etc.) prompt: Text description of the desired transformation. Examples: "Make it black and white", "Add a rainbow in the sky", "Convert to watercolor painting style" model_id: Model identifier from list_models(). Examples: "amazon.nova-canvas-v1:0", "gpt-image-1.5", "models/gemini-2.5-flash-image" output_path: File path where the transformed image will be saved. Parent directories are created automatically. Returns: Success message with output path, or error description. |
| prompt_guideA | Get best practices and examples for writing effective image generation prompts. Returns guidelines for crafting detailed prompts that produce better results. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
The tools are mostly distinct: list_models, generate_image, transform_image, and prompt_guide each have clear roles. However, generate_image's optional reference_image overlaps with transform_image's purpose of editing an existing image, which could cause confusion when choosing between them.
The first three tools follow a consistent verb_noun pattern (list_models, generate_image, transform_image). The exception is prompt_guide, which is a noun phrase rather than a verb, creating a minor deviation from the otherwise uniform naming convention.
With 4 tools, the server is well-scoped for its stated purpose. Each tool serves an essential function in the image generation workflow, and the count is neither excessive nor too sparse.
The server covers the core image generation lifecycle: listing models, generating images, and transforming existing ones. A gap is the lack of model capability details beyond IDs, which would help agents choose the right model for a task, but this can be worked around.