imagen-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your OpenAI API key. Set in the environment or in a .env file next to package.json. | |
| IMAGEN_OUTPUT_DIR | No | Default directory for saved images. | ./output |
| IMAGEN_DEFAULT_MODEL | No | Default model to use: flare or sunburst. | flare |
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 OpenAI GPT Image 2.5. Saves files to disk and returns paths and token usage. |
| edit_imageA | Edit, restyle, or combine 1-16 reference images with a text prompt using OpenAI GPT Image 2.5. The whole picture is regenerated; without a mask the prompt decides what changes. Saves files to disk and returns paths and token usage. |
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
generate_image and edit_image are clearly separated by input and intent: one creates a new image from a prompt, the other modifies or combines existing reference images. There is no meaningful scenario where an agent would confuse which tool to call.
Both tool names use the same underscore-separated verb_noun pattern. The naming is consistent and immediately communicates the action and object.
The server exposes only two tools, which is slightly lean relative to the typical 3-15 tool range. However, the count matches the narrow image-generation/editing scope and omits unnecessary extras.
The two tools cover the core lifecycle of the domain: creating an image and editing existing images. Outputs are saved and paths returned, so an agent can continue working with the results.