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To use NanoBanana MCP in Cursor, choose one specific MCP implementation, configure it with a Gemini API key, restart Cursor, and use its image tools to generate a draft and request focused edits. This guide uses petrkindlmann’s nano-banana-mcp, whose README lists Cursor as a compatible client and describes carrying image context between editing turns. It is a community integration, not a uniquely defined product: other projects with similar names use different commands and credential names.
What NanoBanana MCP does in Cursor
Model Context Protocol (MCP) lets a compatible client connect to tools exposed by a server. In this workflow, the community server exposes Google Gemini image generation and editing tools for use through Cursor. The repository documents a generate_image tool and an Interactions API workflow; Google provides the underlying Gemini models.
Cursor’s MCP documentation URL is https://docs.cursor.com/context/mcp. Because its documentation may change, use Cursor’s current MCP guidance to confirm its supported configuration format and location. The community README identifies Cursor as a client, but that is not an official Cursor endorsement.
Choose one implementation and check model access
This walkthrough follows the petrkindlmann repository throughout. Its setup uses a local Node.js checkout and the environment variable GEMINI_API_KEY. Do not mix those instructions with the separate YCSE NanoBanana MCP project, which documents a package and the variable GOOGLE_AI_API_KEY.
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Before setup, check the selected repository’s current requirements and the models available to your Google API key. The petrkindlmann README says its Gemini 3 image model IDs may rotate or retire, and some models may require API-key access. The Gemini Interactions API is described as beta in that repository, so availability and behavior can change.
Match the model to the editing task
Google’s current Gemini API image-generation guide recommends Nano Banana 2.1 for new projects and describes it as a high-efficiency option for conversational editing. Google positions Nano Banana 2 Lite as its fastest and cheapest option, but says it is not optimized for multiple reference images or sequential multi-turn editing. Nano Banana Pro is positioned for complex professional visual tasks. These are Google’s descriptions, not independent quality, speed, or cost comparisons.
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| Google model | Documented model ID | Best-fit description in Google’s guide |
|---|---|---|
| Nano Banana 2.1 | gemini-nano-banana-2.1 |
High-efficiency workhorse for conversational editing; Google recommends it for new projects. |
| Nano Banana 2 Lite | gemini-3.1-flash-lite-image |
Fastest and cheapest positioning; not optimized for multiple references or sequential multi-turn editing. |
| Nano Banana 2 | gemini-3.1-flash-image |
Listed in Google’s model catalog; check the current guide for capabilities and availability. |
| Nano Banana Pro | gemini-3-pro-image |
Premium option positioned for complex professional assets. |
| Legacy Nano Banana | gemini-2.5-flash-image |
Legacy model listed in Google’s guide; check current availability before configuring it. |
Google’s guide describes 1K, 2K, and 4K output across model families, with model-specific exceptions and constraints. Confirm the chosen model’s current resolution options, reference-image support, and access requirements in the API guide rather than assuming every model offers the same controls. The repository’s own defaults may not track Google’s catalog immediately.
Set up the server for Cursor
The steps below follow the petrkindlmann repository’s documented Node.js setup. Its README gives an example configuration path for Claude clients, not a verified, fixed Cursor file path. Cursor’s exact settings location and accepted configuration schema can vary; use the current Cursor MCP instructions to decide where to put the server entry.
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- Get a Gemini API key. Use a key authorized for the image model you intend to call. Keep it private; do not paste it into prompts or commit it to a repository.
- Clone and install the selected server. In a terminal, clone the petrkindlmann repository, change into its directory, and run
npm install. Follow the README if its current requirements or installation instructions differ. - Add the MCP server entry. In the configuration location Cursor currently supports, register the Node command, an absolute path to the cloned repository’s
index.js, and the key in anenvobject. The essential shape is:{ "command": "node", "args": ["/absolute/path/to/nano-banana-mcp/index.js"], "env": { "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY" } }Use your real absolute path and key. This shows the server-entry fields documented by the repository; place them in the complete configuration structure required by your Cursor version.
- Restart Cursor or reload its MCP servers. The repository instructs users to restart the MCP client. Check Cursor’s MCP tools list for the server’s image-generation tools. If it does not appear, first verify the config location and structure, Node.js command, absolute script path, and environment variable spelling.
Generate a first image
Start with a prompt that makes the deliverable easy to inspect. State the image’s purpose, subject, composition, visual style, and aspect ratio; specify a save location if the tool supports one. For example:
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Create a landscape hero image for a travel article about a quiet coastal village.
Show a small harbor in the lower third, pale morning light, and distant cliffs.
Use a calm editorial illustration style with a restrained blue-and-sand palette.
Use a 16:9 aspect ratio and save the result as coastal-hero.png.This is an example prompt, not a guaranteed output specification. Ask Cursor to use the server’s generate_image tool, which the repository documents with a text prompt and optional aspect-ratio and size controls. Inspect the returned image before planning the next change.
Iterate without losing the image’s context
For revisions, identify what should change and what should remain stable. The repository recommends multi-turn editing: each result carries an interaction_id, and passing it back preserves the conversation context. In practical terms, ask Cursor to continue from the prior image interaction rather than starting a new generation when you want a consistent revision.
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- Inspect the draft. Note the most important mismatch, such as lighting, background complexity, or an object’s placement.
- Request a small, explicit change. For example: Keep the same harbor, framing, palette, and illustration style. Make the sky warmer and remove the sailboat on the right; leave all other elements unchanged.
- Continue the prior interaction. Ask Cursor to pass the previous
interaction_idas the repository’s multi-turn workflow describes. - Review the new result. If it is close, make another narrow request. If the direction is wrong, describe the desired correction clearly rather than stacking unrelated changes.
Changing one or a few variables per turn makes it easier to judge whether a revision helped. The repository documents context carry-forward, but it does not guarantee that every requested detail will be preserved exactly.
Use reference images and repeatable styles when needed
The server README describes reference-image inputs for workflows such as compositing, style transfer, product placement, restoration, and attribute replacement. It also describes chaining generations for icon sets. Treat these as documented capabilities, not guaranteed results: check the selected model’s reference limits and provide clear instructions about which qualities to borrow from each reference.
For a consistent series, state the shared visual rules in each request or carry them forward through the same interaction: palette, line weight, perspective, background treatment, and subject framing. If a task depends on multiple references or successive revisions, avoid choosing Nano Banana 2 Lite solely for its speed or cost positioning; Google says that model is not optimized for those workflows.
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Check and export the final artifact
Before using an image downstream, inspect the actual file and confirm its dimensions, format, composition, and any project-specific requirements. Google states that “All generated images include a SynthID watermark.” Account for that watermark in the intended use and review applicable platform or publication requirements.
Model names and IDs can change, and repository defaults can lag behind Google’s catalog. When generation fails or a model is unavailable, check the current Google API guide for the exact model ID and account access, then check the repository’s current configuration options. Do not assume a model identifier from an older setup example will remain valid.
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