mcp-imagenate
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| BFL_API_KEY | No | BFL FLUX API key | |
| GEMINI_API_KEY | No | Google AI Studio API key | |
| OPENAI_API_KEY | No | OpenAI API key | |
| GPT_IMAGE_API_KEY | No | Alternative to OPENAI_API_KEY (takes precedence) | |
| NANO_BANANA_API_KEY | No | Alternative to GEMINI_API_KEY (takes precedence) | |
| NANO_BANANA_OUTPUT_DIR | No | Base directory for saved images. When set, all output and input paths are sandboxed within this 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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | Generate images using multiple providers (Google Gemini, OpenAI, BFL FLUX, Reve). Images are saved to disk and the file paths are returned. |
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 confusion between tools. The single 'generate_image' tool has a clear and distinct purpose.
The tool name follows a clear verb_noun pattern ('generate_image'), which is consistent and descriptive.
Having only one tool feels too few for a general image generation server, even if it supports multiple providers. Users may expect additional capabilities like model listing or image management.
The tool covers the core functionality of generating images from multiple providers and saving them, which is the stated purpose. Minor gaps like model selection or deletion are not critical for basic use.