DataGov Israel MCP Server

An MCP server for exploring Israeli government open data (data.gov.il) — with built-in interactive visualizations powered by MCP Apps.

Search thousands of public datasets, profile their structure, generate charts, and plot geographic data on maps — all from your AI assistant.

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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:

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Quick Start

Installation

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

fastmcp install claude-desktop server.py

Restart Claude Desktop — you'll see the DataGovIL tools available immediately.

Try It with the MCP Inspector

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:

fastmcp dev apps server.py

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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.

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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
)

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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)

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Architecture

MCP Apps

Visualization tools use FastMCPApp providers with 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

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Development

Running Tests

pytest tests/ -v          # 39 tests
pytest tests/ --cov=datagov_mcp  # With coverage

Code Style

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

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Submit a pull request

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Troubleshooting

Port Conflicts with MCP Inspector

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.

Import Errors

uv pip install -e ".[dev]"

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License

MIT — see LICENSE.

Acknowledgments

• Built with FastMCP and prefab-ui
• Data from Israel Open Data Portal
• Maps powered by Leaflet + CARTO
• Charts powered by Recharts (via prefab-ui)