A Practical Guide to Adding Nano Banana Image Tools to Claude Desktop, VS Code, and Cursor

Ace Data Cloud's Nano Banana MCP server connects image generation and editing tools to AI clients such as Claude Desktop, VS Code, and Cursor, enabling assistants to create and refine images directly within a coding environment. The server supports the nano-banana, nano-banana-2, and nano-banana-pro models, and requires an ACEDATACLOUD_API_TOKEN for authentication. Installation and configuration steps are documented for each client, emphasizing secure token handling.

If your coding assistant can already read files, run commands, and reason about a project, the next useful step is often visual: generating mockups, editing product shots, or iterating on image assets without leaving the IDE. Nano Banana MCP is an MCP server for connecting image generation and image editing tools to AI clients such as Claude Desktop, VS Code, and Cursor. Once it is configured, the assistant can call a small set of image-focused tools during a normal conversation instead of forcing you to switch to a separate image UI. The documented tool surface is intentionally compact: nanobanana generate image — generate images from text prompts nanobanana edit image — edit or combine existing images nanobanana get task — query the status of one task nanobanana get tasks batch — query multiple task statusesThe server supports the nano-banana , nano-banana-2 , and nano-banana-pro models. That makes it a good fit for builder workflows where you want to move from a text idea to an image, then keep refining that image in the same chat. Typical examples from the integration guide include prompts like: Those examples are useful because they show the real shape of the workflow: the user describes the image task in natural language, and the MCP client routes the request to the available Nano Banana tool. MCP, or Model Context Protocol, gives AI clients a standard way to call external tools. In this setup, the local MCP server is mcp-nanobanana-pro . Your client starts that command, passes an Ace Data Cloud token through the ACEDATACLOUD API TOKEN environment variable, and then exposes the Nano Banana tools to the assistant. The basic installation path is: pip install mcp-nanobanana-pro If you prefer installing from source, the documented path is: git clone https://github.com/AceDataCloud/NanoBananaMCP.git cd NanoBananaMCP pip install -e . After installation, the command your client needs to run is: mcp-nanobanana-pro The important thing is not to hard-code secrets in prompts or project files you plan to commit. Treat ACEDATACLOUD API TOKEN like any other API token: keep it local, rotate it if needed, and avoid pasting it into public issues or screenshots. For Claude Desktop, edit the client configuration file. The documented locations are: ~/Library/Application Support/Claude/claude desktop config.json %APPDATA%\\Claude\\claude desktop config.json Add an MCP server named nanobanana : { "mcpServers": { "nanobanana": { "command": "mcp-nanobanana-pro", "env": { "ACEDATACLOUD API TOKEN": "Your API Token" } } } } If you use uvx and do not want to install the package in advance, the guide also documents this version: { "mcpServers": { "nanobanana": { "command": "uvx", "args": "mcp-nanobanana-pro" , "env": { "ACEDATACLOUD API TOKEN": "Your API Token" } } } } Save the file, restart Claude Desktop, and start with a small request. For example, ask it to generate a simple icon concept or edit one existing image. A small first test makes it easier to verify that the server starts correctly and that the token is available to the process. For VS Code and Cursor, create .vscode/mcp.json in the project root: { "servers": { "nanobanana": { "command": "mcp-nanobanana-pro", "env": { "ACEDATACLOUD API TOKEN": "Your API Token" } } } } The uvx version is similar: { "servers": { "nanobanana": { "command": "uvx", "args": "mcp-nanobanana-pro" , "env": { "ACEDATACLOUD API TOKEN": "Your API Token" } } } } This project-level setup is nice when the visual workflow belongs to a specific repository. For example, a frontend repo might use it for hero image drafts, empty-state illustrations, or product-placement experiments. A docs repo might use it to generate tutorial covers and diagrams. Because the MCP config lives with the workspace, the assistant has the right tool available where the work happens. Here is a simple builder-oriented loop: nanobanana generate image . nanobanana edit image . nanobanana get task if the client needs to check task progress.For example, in a product UI project you might say: Generate a clean dashboard illustration for a dark-mode SaaS landing page. Use a minimal terminal panel, API cards, and a blue/green accent palette. Then follow up with: Edit the image so the API cards are less crowded and the terminal panel is more prominent. That is where MCP feels useful: the same assistant that understands your implementation context can also help you iterate on visual assets. Nano Banana MCP is not a replacement for design judgment, but it is a practical way to bring image generation and editing closer to where builders already work: Claude Desktop, VS Code, and Cursor. Start with a narrow use case, keep prompts specific, and treat the generated output as a draft you can refine. The full setup reference is in the Ace Data Cloud Nano Banana MCP documentation: https://platform.acedata.cloud/documents/nano-banana-mcp https://platform.acedata.cloud/documents/nano-banana-mcp