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  3. MCPNanoBanana
MCPNanoBanana logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 11:45:59 AM

MCPNanoBanana

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View Repository7 GitHub StarsTotal stargazers on GitHub for the source repository (7 stars).Visit Website
image-generationimage-editingnano-bananavirtual-try-onproduct-placement

Generates and edits images through Google's Nano Banana model, with task status tools and virtual try-on workflows.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ā–¾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "acedatacloud-mcpnanobanana": {
      "command": "uvx",
      "args": [
        "mcp-nanobanana-pro"
      ]
    }
  }
}

šŸ’” Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (4) Directory Badge Claim listing AlternativesšŸŽØ More in Art & Culture

Overview

AceDataCloud/MCPNanoBanana MCP server exposes Google's Nano Banana image generation and editing capabilities through MCP. It sends requests through the AceDataCloud API and provides tools for checking individual or batch task results. Use it from a hosted MCP endpoint or run the Python package locally in stdio or HTTP mode. It fits workflows such as text-to-image generation, virtual try-on, and product placement in supplied scenes.

Use cases

•Generate images from detailed text prompts
•Edit or combine supplied image URLs
•Create virtual try-on compositions
•Place products in realistic scenes
•Track multiple image tasks in one request

Key features

•Nano Banana text-to-image generation
•AI image editing and compositing
•Virtual try-on workflows
•Product placement in scenes
•Single-task and batch task tracking

Capabilities & Tool Schemas (4) ~84 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server — may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by MCPNanoBanana.

nanobanana_generate_image

Generate an AI image from a text prompt using Google's Nano Banana model.

nanobanana_edit_image

Edit or combine images using AI based on a text prompt.

nanobanana_get_task

Query the status and result of an image generation or edit task.

nanobanana_get_tasks_batch

Query multiple image generation/edit tasks at once.

How MCPNanoBanana works

What AceDataCloud/MCPNanoBanana MCP server does

AceDataCloud/MCPNanoBanana MCP server connects MCP-compatible clients to Google's Nano Banana model through the AceDataCloud API. It supports generating an image from a text prompt, editing an existing image, and combining multiple images according to a prompt. The README also identifies virtual try-on and product placement as supported use cases.

The server is intended for conversational or agent-driven image workflows. For example, an agent can create an image of a described scene, place a clothing item on a person using image URLs, or put a product into a realistic environment. Prompt quality matters: the documentation recommends describing the subject, atmosphere, lighting, camera or lens style, and visual quality characteristics.

How it works

Requests are exposed as MCP tools. Image generation and editing return tasks that can be checked later, rather than requiring the client to perform the underlying image operation directly. nanobanana_get_task checks one task, while nanobanana_get_tasks_batch checks multiple generation or editing tasks at once.

The project offers a hosted endpoint at https://nanobanana.mcp.acedata.cloud/mcp. Hosted requests use a Bearer token from AceDataCloud. Claude.ai can connect through an OAuth flow, while the documented configurations for Claude Desktop, Cursor, Windsurf, Cline, and other clients use the endpoint with an authorization header.

Setup and configuration

AceDataCloud/MCPNanoBanana MCP server can be used without local installation through the hosted endpoint. Create an account on the AceDataCloud Platform, obtain an API token from the Nano Banana API documentation page, and provide that token to the MCP client.

For local use, install the mcp-nanobanana-pro package from PyPI or run it with uvx. The local process reads the token from ACEDATACLOUD_API_TOKEN and starts in stdio mode by default. HTTP mode is available with --transport http --port 8000. The README also documents a Docker image at ghcr.io/acedatacloud/mcp-nanobanana-pro:latest for self-hosting.

A local Claude Desktop configuration uses uvx with the package name in args and passes the API token through the environment. Hosted configurations instead specify the streamable HTTP URL and an Authorization: Bearer header.

Tools and capabilities

The four documented tools are:

  • nanobanana_generate_image: Creates an image from a text prompt using Nano Banana.
  • nanobanana_edit_image: Edits or combines images based on a text prompt.
  • nanobanana_get_task: Retrieves the status and result of one image task.
  • nanobanana_get_tasks_batch: Retrieves statuses and results for multiple image tasks.

These capabilities cover text-to-image creation, image modification, image compositing, virtual try-on, and product-scene composition when the required image inputs are supplied.

Limitations and notes

The server depends on the AceDataCloud API and requires an AceDataCloud token for authenticated requests. The provided material does not specify API pricing, image limits, supported output formats, task-duration guarantees, or model parameter details. The hosted health-check endpoint does not require authentication, but MCP operations do.

The documentation describes image inputs as image URLs in its virtual try-on example. It does not establish that local file paths or binary uploads are accepted. Client compatibility beyond the explicitly documented configurations should be verified against the relevant MCP client.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
7
Stargazers on the source repository.
Last commit
5d ago
Most recent push to the default branch.
Tools exposed
4
Callable tools this server registers over MCP.
Directory activity
4 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about MCPNanoBanana

Install it from PyPI with `pip install mcp-nanobanana-pro`, or run it directly with `uvx mcp-nanobanana-pro`.

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Technical Specs & Signals

CategoryšŸŽØArt & Culture
PricingBring your own API key (usage-based cost)
More technical detailsExpand ā–¾
TransportSTDIO
RuntimePython
AuthAPI key
LicenseMIT
ClientsClaude Desktop, Cursor, Windsurf, Cline / VS Code
Last updatedSep 5, 2026
10/10 checks healthy over the last 31d
Views4
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars7
GitHub Star CountTotal stargazers on GitHub representing community popularity (7 stars).
Last commit5d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 5, 2026
57Quality signal: Good Ā· 57/100How this signal is calculated ā–¾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools26/30
Adoption & activity6/15
Community engagement1/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 16d ago via OSV.dev Ā· mcp-nanobanana-pro (PyPI)

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