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How to Use NanoBanana MCP in Cursor for Practical Image Editing Workflows

A practical guide to configuring one NanoBanana MCP implementation in Cursor and iterating on Gemini-generated images without mixing project instructions.
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You can use NanoBanana MCP in Cursor to send image-generation and editing requests to Google’s Gemini models, then refine an image over multiple turns. The name refers to several community projects, not one standardized product. This guide uses petrkindlmann’s nano-banana-mcp throughout; its README lists Cursor as a compatible client and describes carrying image context between edits. Check the repository’s current setup and model availability before installing, since package instructions and model IDs can change.

What NanoBanana MCP does in Cursor

MCP is the integration layer: the server exposes image tools that an MCP client such as Cursor can call. In this example, the server connects Cursor to Google Gemini image generation and editing. It is a community integration, not an official Cursor feature or endorsement. The project describes Gemini’s Interactions API as beta, and the precise tools and setup depend on which NanoBanana MCP implementation you choose.

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Use one repository’s instructions from beginning to end. For example, YCSE’s separate @ycse/nanobanana-mcp documents Cursor configuration paths and a different API-key variable. Do not copy that project’s command or key name into the petrkindlmann setup below.

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Set up the petrkindlmann server

Check the prerequisites

  • A Gemini API key, obtained through Google’s developer platform.
  • Node.js and npm, with versions that meet the repository’s current requirements.
  • The current repository contents and model access for your API key. The project warns that some model IDs may require access and that beta or preview IDs can rotate or retire.

Install and register the server

  1. Clone the petrkindlmann repository and follow its current README. From the cloned project directory, run npm install.
  2. In Cursor’s MCP configuration, add a server entry using command set to node, the absolute path to the repository’s index.js as its argument, and GEMINI_API_KEY in the entry’s env object. Use the configuration format and location currently supported by your Cursor installation; the repository’s example client path is not a verified Cursor path.
  3. Restart Cursor or reload its MCP servers, as applicable, and check that the server’s tools are available before asking Cursor to generate an image.

Keep the API key private: do not paste it into prompts, commit it to the repository, or expose it in screenshots. If the server does not appear, verify the absolute script path, environment variable spelling, key access, and the current repository instructions before trying a different project’s configuration.

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Generate a first image

Be specific about the image’s job, subject, composition, style, and aspect ratio. A request might read:

Create a landscape hero image for a home-gardening article. Show a person tending a small balcony garden at early morning, with the gardener and planters on the right and uncluttered soft-green space on the left for headline text. Use natural editorial photography, gentle side lighting, and a 16:9 composition. Save the result as balcony-garden-hero.png in the project’s images folder.

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The project documents a generate_image tool with a text prompt and optional aspect-ratio and size controls. The prompt above is an example of how to specify a result; the exact controls exposed depend on the installed server and its current configuration.

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Refine the image without losing its direction

After reviewing the generated image, make a focused request that names what should change and what should remain. For example: “Keep the same gardener, camera angle, and 16:9 composition. Make the morning light warmer and simplify the background by removing the hanging decorations.” A narrow edit makes it easier to judge whether the revision addressed the issue than asking for several unrelated changes at once.

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The petrkindlmann project recommends multi-turn editing: each result carries an interaction_id, and passing that ID into the next request keeps the conversation context available for continuity. In practice, ask Cursor to use the prior interaction ID when requesting a revision, rather than starting a fresh generation. If your workflow or tool response does not expose an interaction ID, consult the repository’s current instructions; continuity should not be assumed across unrelated requests.

Use reference images and repeatable styles

The server README describes reference-image inputs for workflows such as compositing, style transfer, product placement, restoration, and attribute replacement. These are documented capabilities, not guarantees that every reference or prompt will produce a particular result. State what each reference contributes—for example, “use this photo for the product shape, but do not copy its background”—and specify which parts of the existing composition to preserve.

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For a family of related assets, the repository also describes chained generation for icon sets. Define the shared visual rules first, such as palette, line weight, perspective, and background treatment, then request each asset in the chain. Inspect the outputs for consistency rather than assuming a shared prompt alone will make them identical.

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Choose a Gemini image model for the task

Google’s current image-generation guide describes these model IDs and positions them for different needs. These are Google’s descriptions, not independent quality, speed, or price tests. Verify the exact ID, access requirements, supported resolutions, and current pricing in Google’s documentation and in the server implementation before relying on a model.

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Model Google’s stated positioning Workflow consideration
Nano Banana 2.1 (gemini-nano-banana-2.1) Recommended by Google for new projects; a high-efficiency option for conversational editing. A starting point for iterative image work, subject to availability in the chosen server.
Nano Banana 2 Lite (gemini-3.1-flash-lite-image) Google describes it as the fastest and cheapest option. Google says it is not optimized for multiple reference images or sequential multi-turn editing, so it may not suit a continuity-heavy workflow.
Nano Banana 2 (gemini-3.1-flash-image) Listed in Google’s current image-generation guide. Check the guide for current model-specific capabilities, access, and resolution limits.
Nano Banana Pro (gemini-3-pro-image) Google positions it as a premium option for complex visual tasks. Consider it when the task needs the capabilities described in Google’s current guide; confirm current availability and pricing.
Legacy Nano Banana (gemini-2.5-flash-image) Listed as a legacy model in Google’s guide. Do not assume it is the right default for a new setup; check its current status and access.

Google’s guide describes 1K, 2K, and 4K output across model families, with model-specific differences. It also describes reference-image limits that vary by model and configuration. Do not infer that every listed model supports every resolution or the same number or types of references: check the current Gemini API image-generation documentation. The repository’s own defaults may not track Google’s current catalog, so confirm both the API model ID and the server’s configuration.

Check and export the final image

Review the output at its intended size and check composition, visible artifacts, text, and whether the requested edits were actually applied. Google states that “All generated images include a SynthID watermark.” Account for that watermark in downstream use and review the requirements of the platform or workflow where you plan to publish the image.

Troubleshoot common setup and iteration problems

  • Cursor does not show the server: Confirm the current MCP configuration location and format for your Cursor version, check the absolute path to index.js, and restart or reload the client.
  • Authentication or model errors: Verify that GEMINI_API_KEY is present in the server environment and that the key has access to the requested model. Check Google’s current API guide for model availability.
  • A model ID no longer works: Preview and beta IDs may rotate or retire. Check the repository’s current defaults and Google’s current model list rather than reusing an old configuration blindly.
  • An edit changes too much: Ask for fewer changes in each turn, explicitly name the composition or subject to preserve, and pass the previous interaction_id when the tool supports it.

Sources and configuration references

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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