# Clone or navigate to the project directory
cd data-mcp

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On macOS/Linux
# .venv\Scripts\activate   # On Windows

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

# Test MCP server functionality
python examples/walkthrough/demo_mcp_usage.py

# Launch 3D viewer (opens at localhost:8080)
python -c "
from src.data_mcp.viewers.vtk_viewer import VTKViewer
VTKViewer.show_file('examples/sample_data/gaussian_simple.vti')
"

# Start the MCP server (requires MCP client to connect)
python -m data_mcp.server

{
  "mcpServers": {
    "data-mcp": {
      "command": "python",
      "args": ["-m", "data_mcp.server"],
      "cwd": "/Users/patrick.oleary/code/AI Experiments/data-mcp",
      "env": {}
    }
  }
}

# Test all 10 MCP tools with detailed output
python examples/walkthrough/manual_tool_test.py

# Test format-specific metadata adaptation
python examples/walkthrough/test_format_adaptation.py

# Full MCP workflow testing
python tests/integration/test_full_mcp_workflow.py

# Real MCP client connection test
python tests/integration/test_real_mcp_client.py

from data_mcp.formats.vtk_factory import VTKHandlerFactory
from data_mcp.core.dataset import Dataset
from data_mcp.viewers.vtk_viewer import VTKViewer

# Load dataset with format-specific handler
handler = VTKHandlerFactory.create_handler("path/to/file.vti")
dataset = Dataset("path/to/file.vti", handler)
dataset.introspect()

# Get comprehensive information
info = dataset.get_info()
components = dataset.list_components()
stats = dataset.get_statistics("temperature")

# Launch interactive viewer (convenience method)
VTKViewer.show_file("path/to/file.vti")  # Opens at localhost:8080

# Or create viewer with dataset
viewer = VTKViewer(dataset=dataset)
viewer.show()

data-mcp/
├── README.md                    # Project documentation
├── MCP_WALKTHROUGH.md          # Comprehensive walkthrough guide
├── pyproject.toml              # Python packaging configuration
├── requirements.txt            # Dependencies
├── src/data_mcp/              # Main package
│   ├── server.py              # MCP server implementation
│   ├── core/                  # Core functionality
│   │   ├── dataset.py         # Dataset abstraction with cleanup
│   │   ├── introspector.py    # Dataset analysis engine
│   │   ├── schema.py          # Schema representation
│   │   └── visualizer.py      # Visualization engine
│   ├── formats/               # Format handlers (inheritance system)
│   │   ├── base.py           # Base format handler interface
│   │   ├── vtk_base.py       # Base VTK handler
│   │   ├── vtk_imagedata.py  # VTI handler (structured grids)
│   │   ├── vtk_unstructured.py # VTU handler (irregular meshes)
│   │   ├── vtk_polydata.py   # VTP handler (surface meshes)
│   │   └── vtk_factory.py    # Handler factory
│   ├── viewers/               # Trame-based visualization
│   │   └── vtk_viewer.py     # VTK 3D viewer
│   └── utils/                 # Utilities
│       └── file_utils.py     # File handling
├── examples/                  # Usage examples
│   ├── basic_usage.py        # Basic programmatic usage
│   ├── walkthrough/          # Walkthrough examples
│   │   ├── demo_mcp_usage.py # Basic MCP demo
│   │   ├── manual_tool_test.py # All 10 tools test
│   │   └── test_format_adaptation.py # Format adaptation demo
│   └── sample_data/          # Sample VTK files
│       ├── gaussian_simple.vti # 3D structured grid
│       └── wave_pattern.vti   # Wave pattern data
└── tests/                     # Test suite
    ├── integration/           # Integration tests
    └── test_formats/         # Format handler tests