AI Watercolor Generator MCP

MCP serverEverything else

Create watercolor art from text or transform existing images into watercolor paintings with AI.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use AI Watercolor Generator MCP

From the project's README

As published by AIWatercolorGenerator/ai-watercolor-generator-mcp in README.md.

Create watercolor art from text or transform local images with AI from any MCP client that supports local stdio servers.

This package is the official local MCP server for AI Watercolor Generator. It is a small, open-source client for the production API: generation, storage, credits, rate limits, and task processing remain on the hosted service.

Features

  • Generate watercolor artwork from a text prompt.
  • Upload a local JPEG, PNG, or WebP image for editing.
  • Transform uploaded images into watercolor paintings.
  • Poll asynchronous tasks until an output is ready.
  • Use the same account and credits as the REST API and hosted MCP server.

Requirements

Keep your API key private. Do not commit it to a repository or paste it into an issue.

Quick start

Run the MCP server through npm without a global installation:

AIWATERCOLOR_API_KEY=YOUR_AIWATERCOLOR_API_KEY \
  npx -y @ai-watercolor-generator/mcp

The process communicates over stdio, so it is normally launched by an MCP client rather than used interactively.

Client configuration

Codex

Codex CLI, the Codex IDE extension, and the ChatGPT desktop app share MCP configuration on the same Codex host. Add this to ~/.codex/config.toml and provide AIWATERCOLOR_API_KEY in the environment that starts Codex:

[mcp_servers.ai_watercolor_generator]
command = "npx"
args = ["-y", "@ai-watercolor-generator/mcp"]
env_vars = ["AIWATERCOLOR_API_KEY"]

Restart the client, then run codex mcp list or use /mcp to verify the connection. See the official Codex MCP documentation.

Cursor

Add this server to your project .cursor/mcp.json or global MCP configuration:

{
  "mcpServers": {
    "ai-watercolor-generator": {
      "command": "npx",
      "args": ["-y", "@ai-watercolor-generator/mcp"],
      "env": {
        "AIWATERCOLOR_API_KEY": "YOUR_AIWATERCOLOR_API_KEY"
      }
    }
  }
}

See the Cursor MCP documentation.

Claude Desktop and other JSON-based clients

Use the same stdio configuration in the client's local MCP settings:

{
  "mcpServers": {
    "ai-watercolor-generator": {
      "command": "npx",
      "args": ["-y", "@ai-watercolor-generator/mcp"],
      "env": {
        "AIWATERCOLOR_API_KEY": "YOUR_AIWATERCOLOR_API_KEY"
      }
    }
  }
}

Restart the client after saving. Claude Desktop may present local MCP servers through its Extensions interface; see Anthropic's local MCP server guide for the current setup flow.

Tools

ToolPurpose
generate_watercolorSubmit an asynchronous text-to-watercolor task
upload_watercolor_inputUpload a local JPEG, PNG, or WebP file up to 10 MiB
edit_watercolorSubmit an asynchronous watercolor edit using uploaded image URLs
get_watercolor_taskRead task status, outputs, or failure details

Generate from text

Call generate_watercolor with a prompt. Optional fields are model, aspect_ratio, resolution, and idempotency_key.

{
  "prompt": "A red cottage beside a quiet lake, soft wet-on-wet washes",
  "model": "watercolor-lite",
  "aspect_ratio": "4:3",
  "resolution": "1k"
}

The tool returns a task with status queued or processing. Call get_watercolor_task every 2–5 seconds until the status is succeeded, failed, or canceled.

Edit a local image

First call upload_watercolor_input with an explicit local file path:

{
  "file_path": "/absolute/path/to/source-image.png"
}

Then pass the returned url to edit_watercolor:

{
  "prompt": "Preserve the composition with soft transparent watercolor washes",
  "input_images": [
    "https://cdn.aiwatercolorgenerator.com/api-inputs/.../upload.png"
  ],
  "model": "watercolor-lite",
  "aspect_ratio": "auto",
  "resolution": "1k"
}

The upload tool reads only the path explicitly supplied in the tool call. It does not scan directories or expand glob patterns. The image is uploaded to AI Watercolor Generator's trusted storage and is then processed by the hosted API.

Models and credits

The API currently supports watercolor-lite, nano-banana-2, nano-banana-pro, and gpt-image-2. Model, resolution, and operation affect credit cost. Failed or canceled tasks receive the refund defined by the hosted API.

See the API documentation for current models, credit costs, limits, error codes, and schemas.

How it relates to the hosted MCP server

The official MCP Registry entry is com.aiwatercolorgenerator/watercolor and supports two installation styles:

  • Hosted Streamable HTTP: https://www.aiwatercolorgenerator.com/api/mcp
  • Local stdio: this npm package

Use the hosted endpoint when your client supports authenticated Streamable HTTP. Use this local package when you need stdio compatibility or want an MCP tool to upload a local image path.

Security and privacy

  • The API key is read from AIWATERCOLOR_API_KEY and is never intentionally logged.
  • Local images are sent to the hosted upload API when you invoke upload_watercolor_input.
  • Tool calls consume account credits and are subject to API rate and concurrency limits.
  • Review file paths and tool calls before approving them in your MCP client.
  • Report vulnerabilities according to SECURITY.md.

Development

pnpm install
pnpm format:check
pnpm typecheck
pnpm test
pnpm pack --dry-run

The test suite mocks HTTP requests and includes a real subprocess handshake against the built stdio executable. No production API key is needed for normal development tests.

Links

License

MIT

Advanced
Delivery
watercolor MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
Catalog kind
mcp-server
Gateway key
com-aiwatercolorgenerator-watercolor
Source
github.com/AIWatercolorGenerator/ai-watercolor-generator-mcp
Hosted endpoint
https://www.aiwatercolorgenerator.com/api/mcp