Nano Banana MCP Server (CLIProxyAPI Edition)
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NO_PROXY | No | Hosts to exclude from proxy (e.g., 127.0.0.1,localhost) | |
| LOG_LEVEL | No | Logging level: DEBUG, INFO, WARNING, ERROR | INFO |
| GCP_REGION | No | Google Cloud region for Vertex AI | us-central1 |
| LOG_FORMAT | No | Log format: standard, json, or detailed | standard |
| GCP_PROJECT_ID | No | Your Google Cloud project ID (required for Vertex AI authentication) | |
| GEMINI_API_KEY | No | Your Gemini API key for authentication | |
| CLIPROXY_CONFIG | No | Path to CLIProxyAPI config.yaml file | |
| CLIPROXY_API_KEY | No | API key for CLIProxyAPI authentication | |
| IMAGE_OUTPUT_DIR | No | Directory path for saving generated images | ~/nanobanana-images |
| NANOBANANA_MODEL | No | Model selection: 'flash', 'pro', or 'auto' | auto |
| CLIPROXY_BASE_URL | No | Base URL for CLIProxyAPI (e.g., http://127.0.0.1:8318) | |
| NANOBANANA_AUTH_METHOD | No | Authentication method: 'api_key', 'vertex_ai', or 'auto' (default: auto) | 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
Server capabilities have not been inspected yet.
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. |
| maintenanceB | 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 distinct, non-overlapping purpose: generate_image for image creation/editing, maintenance for system cleanup tasks, show_output_stats for statistics viewing, and upload_file for file uploading. There is no ambiguity in their functions, and an agent can easily select the correct tool based on the task.
The naming is mixed, with generate_image and upload_file using a verb_noun pattern, while maintenance and show_output_stats are more noun-based or descriptive. This inconsistency makes the pattern less predictable, though the names are still readable and clear in intent.
With 4 tools, the count is reasonable for a server focused on image generation and file management. It covers core operations without being overly sparse or bloated, though it might benefit from a few more specialized tools for advanced image editing or configuration.
The tool set covers key workflows: image generation/editing, system maintenance, statistics viewing, and file uploading. Minor gaps exist, such as tools for deleting uploaded files or managing image metadata more directly, but agents can likely work around these with the provided tools.