galaxy_classification_mcp

An MCP (Model Context Protocol) server that lets Claude (or any other MCP-compatible client) classify galaxy images using the Qwen VL (Vision–Language) model hosted on Alibaba Cloud's DashScope platform.

Features

Both tools accept either a public HTTPS URL or an absolute local file path as the image source.

Related MCP server: Qwen Vision MCP Server

Prerequisites

Installation

# 1. Clone the repository
git clone https://github.com/jyshangguan/galaxy_classification_mcp.git
cd galaxy_classification_mcp

# 2. Create and activate a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Copy .env.example to .env and add your API key
cp .env.example .env
# Then edit .env and replace sk-your-api-key-here with your actual API key

Configuration

The server reads your Qwen API key from the environment. Choose one of the following methods:

Method 1: Using a .env file (recommended)

Create a .env file in the project root:

DASHSCOPE_API_KEY=sk-your-actual-api-key-here

The .env file is already in .gitignore to prevent accidentally committing your API key.

Method 2: Environment variable

# Preferred variable name
export DASHSCOPE_API_KEY="sk-..."

# Alternative (both are checked)
export QWEN_API_KEY="sk-..."

You can obtain a free API key from https://dashscope.aliyun.com/ after registering for an Alibaba Cloud account.

Running the server

Stdio transport (default — for Claude Desktop / Claude Code)

python server.py

The server speaks the MCP stdio protocol and is ready to be connected to by Claude Desktop or Claude Code via the configuration below.

SSE transport (for testing with mcp dev)

mcp dev server.py

Connecting to Claude Desktop

Add the following block to your Claude Desktop configuration file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "galaxy-classification": {
      "command": "python",
      "args": ["/absolute/path/to/galaxy_classification_mcp/server.py"]
    }
  }
}

Replace /absolute/path/to/galaxy_classification_mcp/server.py with the actual path on your machine.

Note: The API key should be stored in a .env file in the project directory (see Configuration above). Alternatively, you can pass it directly in the config by adding an "env" block with "DASHSCOPE_API_KEY".

Connecting to Claude Code (CLI)

If you have a .env file with your API key (recommended):

claude mcp add galaxy-classification \
  -- python /absolute/path/to/galaxy_classification_mcp/server.py

Alternatively, pass the API key directly:

claude mcp add galaxy-classification \
  -e DASHSCOPE_API_KEY=sk-... \
  -- python /absolute/path/to/galaxy_classification_mcp/server.py

Example usage in Claude

Once the MCP server is connected you can ask Claude questions like:

Classify the galaxy in this image:
https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/NGC_4414_%28NASA-med%29.jpg/1024px-NGC_4414_%28NASA-med%29.jpg

Claude will call the classify_galaxy tool and return a structured report such as:

Morphological type : Sc (late-type spiral)
Key visual features: Two loosely wound, patchy spiral arms; bright,
                     compact nucleus; clumpy star-forming regions along
                     the arms; no bar visible.
Confidence         : High

Available models

Pass the model argument to either tool to switch models:

Use qwen-vl-plus to classify: https://example.com/galaxy.jpg

Project structure

galaxy_classification_mcp/
├── server.py          # MCP server (FastMCP, Qwen VL tools)
├── requirements.txt   # Python dependencies
├── .env.example       # Example environment variables template
├── .env               # Your actual API key (not in git)
├── pyproject.toml     # Project metadata
└── README.md          # This file

License

See LICENSE.