gpt-image-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your OpenAI API key with credits. Required for image generation. | |
| IMAGE_OUTPUT_DIR | No | Absolute path where generated images will be saved. If not set, images save to ./generated-images relative to the working directory. |
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 |
|---|---|
| generate_imageA | Generate image(s) from a text prompt with OpenAI GPT Image and save them to disk. Use this to create new images from scratch: UI mockups, icons, hero images, diagrams, illustrations, and social/marketing graphics. GPT Image renders in-image text unusually well, so write any text you want to appear in the image literally and describe its placement. Args: prompt: What to generate. Be specific about subject, style, composition, lighting, and any text that should appear in the image. model: GPT Image model. Default "gpt-image-2" (best quality + reasoning). Use "gpt-image-1-mini" for cheap/fast iteration, then re-render the final asset with "gpt-image-2". size: "1024x1024" (square), "1536x1024" (landscape), "1024x1536" (portrait), or "auto". gpt-image-2 also supports larger / 2K sizes. quality: "low", "medium", "high", or "auto". Higher is better and costs more. n: Number of images to generate (1-10). background: "auto", "opaque", or "transparent". NOTE: gpt-image-2 does NOT support "transparent" — pass model="gpt-image-1.5" (or another alpha-capable model) when you need a transparent background. output_format: "png", "jpeg", or "webp". output_dir: Directory to save images into. Defaults to $IMAGE_OUTPUT_DIR, or ./generated-images relative to the current working directory. filename: Base filename without extension. Defaults to a slug of the prompt plus a timestamp. Returns: A text summary with the saved file path(s), followed by the image(s) inline. |
| edit_imageA | Edit, restyle, or combine existing image(s) using a text instruction. Use this for image-to-image work: modify an image, restyle it, composite several inputs into one, inpaint a masked region, or extend / outpaint. Args: prompt: Instruction describing the desired edit or final result. images: One or more paths to input image files (PNG/JPEG/WEBP, <=50MB each). Multiple images are used together as references / inputs. mask: Optional path to a PNG mask. Transparent areas of the mask mark the region to edit/replace; opaque areas are preserved. model: GPT Image model. Default "gpt-image-2". size: "1024x1024", "1536x1024", "1024x1536", or "auto". quality: "low", "medium", "high", or "auto". n: Number of variations to generate (1-10). output_format: "png", "jpeg", or "webp". output_dir: Where to save. Defaults to $IMAGE_OUTPUT_DIR or ./generated-images. filename: Base filename without extension. Returns: A text summary with the saved file path(s), followed by the image(s) inline. |
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 2 tools
The two tools have clearly distinct purposes: one creates new images from scratch, the other edits/restyles existing images. No overlap or ambiguity between them.
Both tools use a consistent verb_noun pattern: generate_image and edit_image. The naming is predictable and follows the same convention.
With only 2 tools, the set feels thin but is appropriate for a focused image generation server. It covers the two core operations without bloat, but is on the low end of the acceptable range.
The domain is image generation/editing, and both primary operations are covered. Minor gaps exist (e.g., no direct list/delete of generated images), but agents can work around those via the filesystem.