Nano Banana MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Logging level: DEBUG, INFO, WARNING, ERROR. Default: INFO. | INFO |
| GCP_REGION | No | GCP region for Vertex AI. Default: 'global'. Use 'us-central1' for legacy 2.5 Flash Image model. | global |
| LOG_FORMAT | No | Log format: standard, json, detailed. Default: standard. | standard |
| GCP_PROJECT_ID | No | Your Google Cloud project ID. Required for Vertex AI authentication. | |
| GEMINI_API_KEY | No | Your Google Gemini API key. Required for API key authentication. | |
| GEMINI_BASE_URL | No | Custom API endpoint for Gemini (e.g., for proxies/gateways). | |
| IMAGE_OUTPUT_DIR | No | Directory for saving generated images. Default: ~/nanobanana-images. | |
| NANOBANANA_MODEL | No | Model tier: 'flash', 'nb2', 'pro', or 'auto' (default: auto → nb2). | auto |
| NANOBANANA_AUTH_METHOD | No | Authentication method: 'api_key', 'vertex_ai', or 'auto' (default: auto). |
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
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | Generate new images or edit existing images using natural language instructions. Supports multiple input modes:
Automatically detects mode based on parameters or can be explicitly controlled. Input images are read from the local filesystem to avoid massive token usage. Returns both MCP image content blocks and structured JSON with metadata. |
| upload_fileA | Upload a local file through the Gemini Files API and return its URI & metadata. Useful when the image is larger than 20MB or reused across prompts. |
| show_output_statsA | Show statistics about the output directory and recently generated images. |
| maintenanceA | Perform maintenance operations following workflows.md patterns. Available operations:
|
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| photorealistic_shot | Generate a prompt for high-quality photorealistic images. |
| logo_text | Generate a prompt for logo creation with accurate text rendering. |
| product_shot | Generate a prompt for studio product photography. |
| sticker_flat | Generate a prompt for flat/kawaii style stickers. |
| iterative_edit_instruction | Generate an instruction for precise image editing. |
| composition_and_style_transfer | Generate an instruction for style transfer and composition blending. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| prompt_templates_catalog | A compact catalog of prompt templates (same schemas as the @mcp.prompt items). |
| list_operations | List all tracked operations. Returns: Dict with list of operations and summary statistics |
TDQS
Scored across 4 tools
Each tool has a clearly distinct role: generation/editing, file upload, output statistics, and maintenance operations. There is no meaningful overlap between them, and the descriptions make the boundaries obvious.
Three tools follow a consistent verb_noun pattern: generate_image, upload_file, show_output_stats. The 'maintenance' tool breaks this pattern by using a bare noun instead of something like run_maintenance or perform_maintenance.
Four tools is well-scoped for an image generation server: one core generation tool, one upload helper, one stats view, and one maintenance tool. Each tool earns its place without unnecessary bloat or redundancy.
The core image generation and editing workflow is covered, along with upload support for large files and basic output monitoring. Minor gaps exist around explicit file deletion or listing of uploaded Files API entries, but these are workable through maintenance and stats tools.