Nano Banana MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| HTTPS_PROXY | No | HTTPS代理地址 | |
| GOOGLE_API_KEY | Yes | Google AI Studio API密钥 |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageC | Generate images using Google Gemini Nano Banana Pro (gemini-3-pro-image-preview) model |
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 ambiguity or overlap between tools. The single tool 'generate_image' has a clearly distinct purpose that cannot be confused with any other tool in this set.
The single tool name 'generate_image' follows a clear verb_noun pattern. With only one tool, there is perfect consistency as there are no other tools to compare against or create naming conflicts with.
A single tool for an image generation server feels thin and under-scoped. While it might cover the core functionality, typical MCP servers for such domains would include additional tools like list_models, get_image_details, or variations of generation parameters. The count of 1 suggests limited capability for agents to perform related operations.
The tool surface is severely incomplete for an image generation domain. There is only a generation tool with no supporting operations like model listing, configuration management, image retrieval, or editing capabilities. This creates significant gaps that will likely cause agent failures when trying to perform comprehensive image-related tasks.