nanobanana-mcp-server: AI image generation MCP server powered by Google Gemini, with smart model selection and 4K output

by zhongweili · MCP Server · ★ 400

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Featured in: Image Generation

How to install nanobanana-mcp-server

uvx nanobanana-mcp-server@latest
pip install nanobanana-mcp-server

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars400
Forks116
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score70/100
Last Updated2026-10-02
Created2025-08-31
Platformsgemini, mcp, python
Est. Tokens~17k

About nanobanana-mcp-server

Nano Banana MCP Server 🍌 A production-ready Model Context Protocol (MCP) server that provides AI-powered image generation capabilities through Google's Gemini models with intelligent model selection. ⭐ NEW: Nano Banana 2 — Gemini 3.1 Flash Image! 🍌🚀 Nano Banana 2 () is now the default model — delivering Pro-level quality at Flash speed: 🍌 Flash Speed + 4K Quality: Up to 3840px at Gemini 2.5...

Compatible Skills

These tools work well together with nanobanana-mcp-server for enhanced workflows:

  • davinci-resolve-mcp — semantic(0.68)+same_lang+similar_pop+shared_platform (59%)
  • MCPTest — semantic(0.94)+same_lang+similar_pop+shared_platform (55%)
  • dify-plugin-mcp_server — semantic(1.00)+same_lang+similar_pop+shared_platform (55%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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