Labnana MCP

labnana-mcp

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An MCP server for the Labnana OpenAPI. It enables Claude, Claude Code, and other MCP clients to generate and edit images with Labnana.

Current OpenAPI models (product names mapped to the model parameter):

¹ wan2.7-image-pro supports 4K only for text-to-image; generations with reference images are limited to 2K. The OpenAPI reference lists GPT-Image-2.5 Flare and Sunburst; the model directory lists 1K / 2K / 4K and 10 aspect ratios, while the integration guide has not yet added the variants to its model list. Both variants support up to four reference images and cost the same. Check estimate_credits for the current price before generation.

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Installation

Requires Node.js 20.9 or later.

Claude Code

1. Create an API key in the Labnana API Keys console.
2. Make LABNANA_API_KEY available in the environment used by Claude Code.
3. Add the server:

claude mcp add labnana -- npx -y @exoticknight/labnana-mcp

The package can also be started directly with:

npx -y @exoticknight/labnana-mcp

DeepSeek Harness (DSH)

Add the server through DSH's official MCP client plugin. Pin 2.2.0 when you want the version shown by initialize to identify this MCP Apps-capable build:

- id: mcp-labnana  name: '@deepseek-ai/dsh-mcp-client'  config:    serverName: labnana    transport: stdio    command: npx    args: ['-y', '@exoticknight/[email protected]']    env:      LABNANA_API_KEY: !!js process.env.LABNANA_API_KEY

DSH's MCP bridge can project supported ImageContent into the calling vision model. Version 2.1 also publishes a standard MCP Apps single-file View for generate_image and get_generation_task. An MCP Apps-capable DSH Web host renders the preview inline; a stock generic DSH tool card may still show the JSON fallback even though the model received the image. This is a client presentation limitation, not a lost generation result.

For a local source checkout, use the same row with command: node, an absolute args path to dist/index.js, and cwd set to this repository. Current stock DSH builds bridge MCP tools but do not consume MCP resources in the generic tool card. Without an MCP Apps host, model vision still works and the card falls back to JSON.

Cursor and VS Code

One-click install (replace the placeholder API key after installing):

[Install in Cursor] [Install in VS Code]

Claude Desktop and other MCP clients

Use an equivalent mcpServers configuration:

{  "mcpServers": {    "labnana": {      "command": "npx",      "args": ["-y", "@exoticknight/labnana-mcp"],      "env": {        "LABNANA_API_KEY": "lh_xxxxxxxxx"      }    }  }}

Local source checkout

npm installnpm run build

Then configure the server with the generated dist/index.js:

claude mcp add labnana -- node <path-to-repo>/dist/index.js

On Windows, use an absolute path such as:

claude mcp add labnana -- node C:/path/to/labnana-mcp/dist/index.js

Configuration

The server reads the following environment variable:

The command-line options below are also supported:

Environment variables are recommended because command-line arguments may be visible in the local process list.

Tools

Usage

Generate an image

{  "name": "generate_image",  "arguments": {    "model": "gemini-3-pro-image",    "prompt": "Change the background of the image to the grasslands of Inner Mongolia",    "referenceImages": [      {        "fileData": {          "fileUri": "https://cdn.labnana.com/xxx.png",          "mimeType": "image/png"        }      }    ],    "imageConfig": {      "imageSize": "2K",      "aspectRatio": "16:9"    }  }}

The default is outputMode=hybrid: the full original is saved to disk while a bounded MCP image preview, structuredContent, and equivalent JSON text are returned together. This provides progressive compatibility across Codex, Claude Code, Claude Desktop, and other MCP clients. Use saveDir to choose the target directory.

All 1K, 2K, 4K, and get_generation_task results use the same envelope:

{  "schemaVersion": 1,  "status": "succeeded",  "taskId": "task-123",  "images": [    {      "index": 0,      "mimeType": "image/png",      "width": 4096,      "height": 4096,      "byteLength": 18442231,      "sha256": "...",      "url": "https://.../original.png",      "filePath": "C:\\...\\labnana.png",      "preview": { "included": true, "mimeType": "image/jpeg", "width": 1600, "height": 1600 }    }  ]}

Image base64 appears only in standard MCP ImageContent, never duplicated into text or structured metadata. Full 4K originals are not inlined; previews have a maximum 1600-pixel edge and target a 2 MiB byte ceiling, while filePath/url locate the original.

Use referenceImages for image-to-image generation and editing. fileData.fileUri supports gs:// and https://; small images can be passed as base64 through inlineData.data.

4K requests automatically run as asynchronous tasks: the server creates the task, polls with rate-limit backoff, downloads and saves originals, and creates previews. If the wait exceeds timeoutSeconds (default 300), the result has status=pending and a taskId; this is not treated as generation failure. Fetch the final preview and original URL later with get_generation_task.

Credit estimation

{  "name": "estimate_credits",  "arguments": {    "prompt": "A Shiba Inu running through a snowy landscape",    "imageConfig": {      "imageSize": "4K"    }  }}

Parameters

• model (default: gemini-3-pro-image): gemini-3-pro-image, gemini-3.1-flash-image, gpt-image-2, gpt-image-2.5-flare, gpt-image-2.5-sunburst, wan2.7-image-pro, wan2.7-image, or seedream-5-0-pro. The provider is derived from the model automatically. Use estimate_credits to confirm a GPT-Image-2.5 size and cost before generation.
• imageConfig.imageSize: 1K, 2K, or 4K. wan2.7-image and seedream-5-0-pro do not support 4K; wan2.7-image-pro also disallows 4K when reference images are present.
• imageConfig.aspectRatio: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, 1:4, 4:1, 1:8, or 8:1. GPT-Image-2 may omit this field and let the service choose; GPT-Image-2.5 supports the ten ratios from 1:1 through 21:9; Wan2.7 supports only 1:1, 16:9, 9:16, 4:3, and 3:4.
• referenceImages: OpenAPI allows up to 14 for Gemini, 4 for GPT-Image-2 / GPT-Image-2.5, 9 for Wan2.7, and 10 for Seedream. These are API limits, not the separate upload limits of the web generator.
• Seedream precise editing: put the source image in referenceImages and describe the target region and change in prompt using absolute coordinates from the top-left origin. OpenAPI has no separate mask or region parameter.
• outputMode (generate_image only): hybrid (default, save originals and inline bounded previews), file (save originals and return metadata only), or inline (do not save to saveDir; return a bounded preview, and persist the original to the default recovery directory only when the upstream response has no original URL).
• saveDir / timeoutSeconds (generate_image only): target directory for hybrid/file mode, and the maximum wait for async (4K) generations.

Credit summary

GPT-Image-2.5 Flare and Sunburst have the same price, but the integration guide does not list their size-specific credit values. Call estimate_credits with the intended model and imageConfig for the current cost and supported configuration.

Free usage

get_subscription returns freeUsages. This quota is shared with the Labnana website and ListenHub app. Nano Banana Pro free usage applies to 1K / 2K only; 4K is always charged in credits. When using that free quota, a never-paid account may receive a retryable busy error on the low-priority lane; retries do not consume the free quota or credits.

Errors

API errors are returned as { code, message } and exposed as MCP results with isError: true.

Development

npm installnpm run typechecknpm test

To inspect the real single-file MCP App without calling Labnana or spending credits, run npm run test:ui and open http://127.0.0.1:4173/test/mcp-app-host.html. The local host sends a generated 640×360 PNG fixture through the official AppBridge so the image, status, and metadata can be checked visually.

For optional live API checks, put LABNANA_API_KEY in the repository-root .env and run npm run test:real. It checks the subscription and estimates both GPT-Image-2.5 variants without generating images or spending credits; the tests skip when the key is not configured.

To verify actual image generation, first run npm run build, then run node test/e2e-generate.mjs. This spends credits. Set LABNANA_TEST_MODEL, LABNANA_TEST_IMAGE_SIZE, LABNANA_TEST_ASPECT_RATIO, LABNANA_TEST_PROMPT, and LABNANA_OUTPUT_DIR to choose the model, output, and save location. Generated originals go under labnana-images/ by default and are ignored by Git.

Links

• Labnana integration guide
• Labnana OpenAPI reference
• Labnana model directory
• Credit guide
• GitHub repository
• Official MCP Registry
• Glama directory
• Technical support: [email protected]

License

Apache-2.0

Community

• LINUX DO
• Official MCP Registry
• Glama
• GitHub Issues