Content
# DataGov Israel MCP Server
An MCP server for exploring Israeli government open data ([data.gov.il](https://data.gov.il)) — with built-in interactive visualizations powered by [MCP Apps](https://gofastmcp.com).
Search thousands of public datasets, profile their structure, generate charts, and plot geographic data on maps — all from your AI assistant.
[](https://github.com/aviveldan/datagov-mcp/actions/workflows/test.yml)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
---
## What Can You Do With This?
### 🏠 Explore the Housing Market
Profile public housing datasets to understand unit sizes, locations, and availability:
See which cities have the most demand in government housing lotteries:
Understand the distribution of apartment sizes across the country:
Track housing unit availability over time:
### 🗺️ Map Public Infrastructure
Visualize education institutions across Israel:
Plot public transport stations:
---
## Quick Start
### Installation
```bash
git clone https://github.com/aviveldan/datagov-mcp.git
cd datagov-mcp
# Create virtual environment and install (requires uv)
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"
```
### Using with Claude Desktop
```bash
fastmcp install claude-desktop server.py
```
Restart Claude Desktop — you'll see the DataGovIL tools available immediately.
### Try It with the MCP Inspector
```bash
fastmcp dev inspector server.py
```
This opens a web UI where you can browse tools, test them interactively, and preview MCP App visualizations in the **Apps** tab.
### Using with `fastmcp dev apps`
Preview the interactive visualization apps locally:
```bash
fastmcp dev apps server.py
```
---
## Example: Finding Real Estate Opportunities
Here's a real workflow for someone exploring the Israeli housing market:
```
You: "Search for discounted housing lottery datasets"
→ package_search(q="דירה בהנחה")
Found: "נתונים תקופתיים - תכנית דירה בהנחה" (Discounted Housing Program)
Resource ID: 7c8255d0-49ef-49db-8904-4cf917586031
You: "Profile this dataset so I can understand what fields are available"
→ dataset_profile(resource_id="7c8255d0-49ef-49db-8904-4cf917586031")
Shows: LamasName (city), Subscribers, Winners, PriceForMeter,
LotteryHousingUnits, ProjectName, Neighborhood...
You: "Show me which cities have the most subscribers competing for units"
→ chart_generator(
resource_id="7c8255d0-49ef-49db-8904-4cf917586031",
chart_type="bar",
x_field="LamasName",
y_field="Subscribers",
title="Housing Lottery Subscribers by City"
)
→ Interactive bar chart rendered in MCP Apps UI
You: "Now show the public housing units — map them and show me sizes"
→ dataset_profile(resource_id="c3a68837-9b7a-4ee7-bd92-130678dc8ae3")
Shows: CityLmsName, NumOfRooms (avg 2.4), Floor, TotalArea (21-110 m²)...
→ chart_generator(
resource_id="c3a68837-9b7a-4ee7-bd92-130678dc8ae3",
chart_type="histogram",
x_field="TotalArea",
title="Distribution of Housing Unit Sizes (m²)"
)
→ Most units are 48-57 m², with a long tail up to 110 m²
```
**Insight:** Cities like Ashkelon and Sderot show 25,000-35,000 subscribers per lottery — that's intense competition. Smaller cities in the periphery (Umm al-Fahm, Nazareth) have far fewer. If you're flexible on location, your odds improve dramatically.
---
## Available Tools
### Core Data Tools
| Tool | Description |
|------|-------------|
| `status_show` | Get CKAN version and site info |
| `license_list` | List available dataset licenses |
| `package_list` | Get all dataset IDs |
| `package_search` | Search datasets with filters and sorting |
| `package_show` | Get detailed metadata for a specific dataset |
| `organization_list` | List all organizations |
| `organization_show` | Get details of a specific organization |
| `resource_search` | Search for resources within datasets |
| `datastore_search` | Query data within a specific resource |
| `fetch_data` | Convenience tool — find dataset by name and fetch its data |
### Visualization Tools (MCP Apps) 📊
These tools render interactive UI directly in MCP-compatible clients.
#### `dataset_profile`
Profile a dataset to understand its structure and quality.
- **Fields detected**: integer, number, string, coordinate
- **Statistics**: min, max, mean, null count, unique values
- **Output**: Interactive DataTable with search/filter
```
dataset_profile(resource_id="c3a68837-9b7a-4ee7-bd92-130678dc8ae3", sample_size=200)
```
#### `chart_generator`
Generate interactive charts from any dataset.
| Chart Type | Use Case |
|------------|----------|
| `histogram` | Distribution of numeric values (e.g., apartment sizes) |
| `bar` | Compare categories (e.g., subscribers per city) |
| `line` | Trends over time (e.g., housing units per lottery) |
| `scatter` | Correlations between two numeric fields |
```
chart_generator(
resource_id="7c8255d0-49ef-49db-8904-4cf917586031",
chart_type="bar",
x_field="LamasName",
y_field="Subscribers",
title="Housing Lottery Subscribers by City",
limit=50
)
```
#### `map_generator`
Plot geographic data on interactive Leaflet maps.
```
map_generator(
resource_id="e873e6a2-66c1-494f-a677-f5e77348edb0",
lat_field="Lat",
lon_field="Long",
limit=500
)
```
---
## Useful Resource IDs
Here are some interesting datasets to get started with:
| Dataset | Resource ID | Good For |
|---------|-------------|----------|
| ✈️ Flights (טיסות) | `e83f763b-b7d7-479e-b172-ae981ddc6de5` | Bar charts by airline |
| 🏠 Public Housing (דיור ציבורי) | `c3a68837-9b7a-4ee7-bd92-130678dc8ae3` | Histograms, profiling |
| 🎰 Housing Lotteries (דירה בהנחה) | `7c8255d0-49ef-49db-8904-4cf917586031` | Bar/line charts |
| 🚌 Transport Stations (תחנות) | `e873e6a2-66c1-494f-a677-f5e77348edb0` | Maps (has Lat/Long) |
| 🏫 Schools (מוסדות חינוך) | `5c5d6bb0-755d-470d-84b6-d7dd3135ba9c` | Maps (UTM_X/UTM_Y) |
---
## Architecture
### MCP Apps
Visualization tools use [FastMCPApp](https://gofastmcp.com) providers with [prefab-ui](https://github.com/PrefectHQ/prefab-ui) components:
- **`DataProfile` app** → `DataTable`, `Metric` components
- **`Charts` app** → `BarChart`, `LineChart`, `ScatterChart`, `Histogram`
- **`Maps` app** → `Embed` with Leaflet HTML
Tools registered via `@app.ui()` automatically get proper MCP Apps metadata and render in compatible clients.
### Async HTTP Layer
All API calls use `httpx.AsyncClient` with:
- 30-second timeout
- Automatic retries for 5xx errors
- Connection pooling
### Data Safety
- Numeric values from CKAN are coerced (handles `"25"` → `25.0`)
- Map popup content is HTML-escaped to prevent XSS
- Line charts are sorted by x-axis for correct rendering
---
## Development
### Running Tests
```bash
pytest tests/ -v # 39 tests
pytest tests/ --cov=datagov_mcp # With coverage
```
### Code Style
```bash
ruff check . # Lint
ruff format . # Format
```
### Project Structure
```
datagov-mcp/
├── datagov_mcp/
│ ├── server.py # Core CKAN tools + provider registration
│ ├── apps.py # FastMCPApp definitions (DataProfile, Charts, Maps)
│ ├── visualization.py # Visualization tools (@app.ui entry points)
│ ├── api.py # CKAN API helper
│ └── client.py # HTTP client
├── tests/ # 39 tests with HTTP mocking
│ ├── test_api.py
│ ├── test_contracts.py
│ ├── test_tools.py
│ └── test_visualization.py
├── screenshots/ # Auto-generated demo screenshots
├── server.py # Entrypoint
└── pyproject.toml
```
---
## Contributing
We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Submit a pull request
---
## Troubleshooting
### Port Conflicts with MCP Inspector
```bash
pip install nano-dev-utils
python -c "from nano_dev_utils import release_ports; release_ports.PortsRelease().release_all()"
```
### Windows + OneDrive
Avoid running installation in OneDrive-synced folders. See [uv#7906](https://github.com/astral-sh/uv/issues/7906).
### Import Errors
```bash
uv pip install -e ".[dev]"
```
---
## License
MIT — see [LICENSE](./LICENSE).
## Acknowledgments
- Built with [FastMCP](https://gofastmcp.com) and [prefab-ui](https://github.com/PrefectHQ/prefab-ui)
- Data from [Israel Open Data Portal](https://data.gov.il)
- Maps powered by [Leaflet](https://leafletjs.com/) + [CARTO](https://carto.com/)
- Charts powered by [Recharts](https://recharts.org/) (via prefab-ui)
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