Nano Banana MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | No | Gemini API key. Either this or GOOGLE_API_KEY is required. | |
| GOOGLE_API_KEY | No | Alternative to GEMINI_API_KEY. | |
| NANOBANANA_OUTPUT_DIR | No | Directory to save generated images. Default: generated_images/ | |
| NANOBANANA_DEFAULT_MODEL | No | Default model to use. Default: gemini-3.1-flash-image-preview | |
| NANOBANANA_MCP_TRANSPORT | No | MCP transport protocol. Default: stdio |
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| nano_banana_modelsA | Return the supported Nano Banana model ids and usage notes. |
| nano_banana_generate_imageA | Generate or edit images with Nano Banana. Pass only |
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 2 tools
The two tools are clearly distinct: one provides metadata about available models and usage notes, while the other performs the actual image generation/editing. There is no overlap or ambiguity between them.
Both tools share the consistent 'nano_banana_' prefix and use snake_case. 'generate_image' follows verb_noun pattern, while 'models' is a simple plural noun rather than a verb-led name, causing a slight inconsistency.
With only two tools, the surface feels thin, but it is understandable for a narrowly scoped server dedicated to one image generation model. It borders on minimal yet is not unreasonable.
The core functionality of generating/editing images is covered, along with model discovery. Missing features like generation history or job status are not obvious gaps for a simple synchronous API, so it is mostly complete.