KOF Nano Banana MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | API key from a Google Cloud project with billing enabled (Gemini API Free Tier does NOT support image generation models). |
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 |
|---|---|
| nanobanana_generate_imageA | Generate an image using Gemini's native image generation (Nano Banana). This tool calls the Gemini API to generate an image based on your text prompt and saves it to the specified path. Models:
Supported Aspect Ratios: 1:1, 16:9, 9:16, 4:3, 3:4 Example: |
| nanobanana_list_queueA | List and validate prompt files in the queue directory. This tool scans the queue directory for markdown prompt files and provides:
Use this to review what will be generated before running process_queue. Example: |
| nanobanana_process_queueA | Process all prompt files in the queue directory and generate images. Modes:
Overwrite Strategies:
After successful generation, prompt files are moved to completed_dir with timestamp. Example: |
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
Each tool has a clearly distinct purpose with no overlap: generate_image creates individual images, list_queue inspects pending files, and process_queue batch processes the queue. The descriptions reinforce these separate functions, making misselection unlikely.
All tools follow a perfect verb_noun pattern with the 'nanobanana_' prefix: generate_image, list_queue, process_queue. This consistent naming convention makes the tool set predictable and easy to understand.
Three tools is ideal for this server's focused scope of image generation queue management. Each tool earns its place by covering distinct aspects of the workflow: single generation, queue inspection, and batch processing.
The tool set covers the core image generation workflow well with generation, inspection, and processing capabilities. A minor gap exists in not providing tools for managing completed images or modifying queue entries, but agents can work around this with file system operations.