A
licenseNot graded
qualityB
maintenanceMCP server that exposes Google Cloud Vertex AI Imagen and Gemini models for image generation, editing, analysis, and transformation via MCP-compatible clients.
2
MIT
Nano Banana MCP
by jiwon86
Related Servers
Alternatives to Nano Banana MCP
No user-submitted related servers found.
Related Servers
- AlicenseAqualityDmaintenanceMCP server for Google Gemini image generation with configurable model support, enabling text-to-image generation, image editing, and iterative refinement.640MIT
- AlicenseAqualityDmaintenanceMCP server for generating and editing images using Google Gemini API, with support for multi-turn iterative refinement.325 npmMIT
- FlicenseNot gradedqualityCmaintenanceAn MCP server for generating and editing images with Google's Gemini image models, exposing a single tool that AI assistants can call to create or edit images using your own API key.1-
- AlicenseAqualityBmaintenanceExposes OpenAI's gpt-image-2 (image generation and editing) as an MCP server for tools like generate_image, edit_image, and iterative edit sessions.621 npm11MIT
- FlicenseNot gradedqualityCmaintenanceRemote MCP server that exposes a generate_image tool allowing Claude to generate images via the Google Gemini API.-
TDQS
A3.9/5.0
Scored across 2 tools
Disambiguation5/5
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.
Naming Consistency4/5
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.
Tool Count3/5
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.
Completeness4/5
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.