DALL-E MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your OpenAI API key (required) | |
| DEFAULT_QUALITY | No | Default quality setting | standard |
| OUTPUT_DIRECTORY | No | Directory to save generated images | ./generated_images |
| DEFAULT_IMAGE_SIZE | No | Default image size | 1024x1024 |
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
| Name | Description |
|---|---|
| generate_imageC | Generate an image using OpenAI's DALL-E 3 model based on a text prompt |
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 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_image' has a clear, distinct purpose that cannot be confused with any other tool in the set.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
A single tool is too few for most server purposes, as it limits functionality and can feel thin. While DALL-E's core function is image generation, typical MCP servers benefit from multiple tools (e.g., 3-15) to handle related operations like listing images, editing prompts, or managing settings, making this count borderline insufficient.
The tool set covers the basic image generation function, but there are notable gaps for a DALL-E server. Missing operations might include listing generated images, editing or deleting images, or handling variations, which could lead to agent workarounds or incomplete workflows in more complex tasks.