openai-image-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_ORG_ID | No | Sent as OpenAI-Organization. | |
| OPENAI_API_KEY | Yes | Your OpenAI API key. Required to use the server. | |
| OPENAI_BASE_URL | No | Proxy or compatible endpoint. | https://api.openai.com/v1 |
| OPENAI_PROJECT_ID | No | Sent as OpenAI-Project. | |
| OPENAI_IMAGE_MODEL | No | Default model when a call doesn't specify one. | gpt-image-2.5-flare |
| OPENAI_IMAGE_OUTPUT_DIR | No | Where images are saved. | ~/Pictures/openai-image-mcp |
| OPENAI_IMAGE_TIMEOUT_MS | No | Request timeout. | 600000 |
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | Generate images from a text prompt with the OpenAI Images API. Choose the model per call. Images are saved to disk and their paths returned. |
| edit_imageA | Edit or combine existing images with a prompt (e.g. "put the product from image 1 on the background from image 2"). Optional mask: transparent pixels mark the area to change. Results are saved to disk. |
| list_modelsA | List supported OpenAI image models with their capabilities, the default model, and retired models. |
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 3 tools
The three tools each target a clearly distinct action: creating new images, editing existing ones, and reading model metadata. An agent can trivially choose between generate_image and edit_image based on whether it has source images, and list_models is purely informational.
All names follow a consistent snake_case verb_noun pattern (generate_image, edit_image, list_models), with plain, predictable verbs and no style mixing.
Three tools is on the lean side but covers the core generate/edit/metadata loop without redundancy. A slightly larger surface (e.g. mask creation, result retrieval) would not feel padded.
The generate/edit/list lifecycle is covered and the descriptions clarify how results land on disk, but there is no way to fetch or clean up generated files, no variation/upscale path, and no way to inspect saved outputs after the fact.