A
licenseA
qualityD
maintenanceEnables AI image generation, editing, and upscaling via Google Gemini and Imagen models, supporting dynamic model switching and multiple MCP-compatible clients.
1
2
MIT
NanoBananaMCP
by AceDataCloud
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TDQS
A4.5/5.0
Scored across 4 tools
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: generate_image creates new images from prompts, edit_image modifies or combines existing images, get_task retrieves single task results, and get_tasks_batch retrieves multiple task results. The descriptions reinforce these distinct roles, making tool selection unambiguous.
Naming Consistency5/5
All tools follow a consistent snake_case pattern with the prefix 'nanobanana_' followed by a clear verb_noun combination (edit_image, generate_image, get_task, get_tasks_batch). The naming convention is perfectly uniform across all four tools, making them predictable and easy to understand.
Tool Count5/5
Four tools is well-scoped for an image generation/editing server, covering core operations: generation, editing, and status retrieval (both single and batch). Each tool earns its place without redundancy, providing a complete yet manageable surface for the domain.
Completeness4/5
The tool set covers the essential lifecycle of image tasks: create (generate_image), modify (edit_image), and retrieve results (get_task and get_tasks_batch). A minor gap exists in lacking explicit deletion or management tools for tasks, but agents can work around this as the core workflows are fully supported.
Maintenance
ActivityActive
ResponsivenessUnresponsive