HDF5-NetCDF-MCP / UI_GUIDE.md
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# HDF5/NetCDF MCP Server - UI Quick Start Guide
## Getting Started
### 1. Launch the Interface
```bash
python app.py
```
Open your browser to: `http://localhost:7860`
---
## 2. Load a File (Required First Step)
The interface has three ways to load a file. **All return a `file_path` that you'll need for every other tool.**
### Option A: Example Datasets (Easiest)
- Navigate to **πŸ“₯ File Loading β†’ Example Datasets**
- Select a dataset (e.g., `small_netcdf`)
- Click **Submit**
- **Copy the `file_path`** from the JSON result (e.g., `"/tmp/tmpxyz/tiny.nc"`)
### Option B: Download from URL
- Navigate to **πŸ“₯ File Loading β†’ Download from URL**
- Paste a URL to an HDF5 or NetCDF file
- Click **Submit**
- **Copy the `file_path`** from the result
### Option C: Upload Local File
- Navigate to **πŸ“₯ File Loading β†’ Local File**
- Select your .nc or .h5 file
- Click **Submit**
- **Copy the `file_path`** from the result
---
## 3. Use Any Tool
Once you have the `file_path`, the workflow for all tools is the same:
1. Navigate to the desired tool tab (Data Analysis or Visualizations)
2. **Paste the `file_path`** into the "File Path" field
3. Enter the dataset/variable name (e.g., "temperature")
4. *Optional*: Add a slice string for subsetting (e.g., "0:10, :, :" for first 10 time steps)
5. Click **Submit**
**Example workflow:**
```
1. Load file β†’ Get file_path: "/tmp/tmpxyz/ocean.nc"
2. Structure & Info β†’ Paste file_path β†’ See available variables: ["temperature", "salinity"]
3. Compute Statistics β†’ Paste file_path β†’ Dataset: "temperature" β†’ Slice: "0, :, :"
4. Heatmap β†’ Paste file_path β†’ Dataset: "temperature" β†’ Slice: "0, :, :" β†’ Download HTML
```
---
## Tips
- **Keep the file_path handy**: Copy it to a text editor - you'll use it repeatedly
- **Start with Structure & Info**: See what variables are available before analyzing
- **Use Preview first**: Check a few values before loading full datasets
- **Download visualizations**: All charts export as interactive HTML files
- **Slice large data**: Use slice strings (e.g., ":, 0:50, :") to work with subsets