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aviveldan

Datagov Israel MCP

by aviveldan
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Python
Remote

DataGov Israel MCP Server

A Model Context Protocol (MCP) server for interacting with the Israeli Government Public API (data.gov.il). Access thousands of public datasets with built-in visualization capabilities.

Tests Python 3.10+ License: MIT

Screenshots

Visualization Tools in Action

Generate interactive charts and maps from Israeli government datasets:

Visualization Examples

Related MCP server: Oracle MCP Server by CData

Features

  • ✅ Async I/O: True async operations with httpx for optimal performance

  • 🔍 Full CKAN API Coverage: Search datasets, organizations, and resources

  • 📊 Visualization Tools: Generate charts and maps from data

  • 🧪 Comprehensive Tests: 34+ automated tests with HTTP mocking

  • 🚀 Modern FastMCP: Built on FastMCP 2.14+ with best practices

  • 📦 Type Safe: Full type hints and validation

Quick Start

Installation

This project uses uv package manager for fast, reliable dependency management.

git clone https://github.com/aviveldan/datagov-mcp.git cd datagov-mcp # Create virtual environment and install uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate uv pip install -e ".[dev]"

Using with Claude Desktop

Install the server directly into Claude Desktop:

fastmcp install claude-desktop server.py

Then restart Claude Desktop and you'll see the DataGovIL tools available!

Testing with MCP Inspector

Launch the interactive inspector to test tools:

fastmcp dev server.py

This opens a web interface where you can:

  • Browse all available tools

  • Test tool calls with different parameters

  • View real-time responses

  • Debug issues

Available Tools

Core CKAN Tools

status_show

Get the CKAN version and installed extensions.

# Example response { "success": true, "result": { "ckan_version": "2.9.0", "site_title": "Israel Open Data Portal" } }

license_list

List all available licenses for datasets.

package_list

Get a list of all dataset IDs.

package_search

Search for datasets with filters.

Parameters:

  • q (string): Query string (e.g., "education")

  • fq (string): Filter query in SOLR format

  • sort (string): Sort order (e.g., "metadata_modified desc")

  • rows (int): Number of results (default: 20)

  • start (int): Pagination offset (default: 0)

  • include_private (bool): Include private datasets (default: false)

Example:

# Search for education-related datasets package_search(q="education", rows=10, sort="metadata_modified desc")

package_show

Get detailed metadata for a specific dataset.

Parameters:

  • id (string, required): Dataset ID or name

Example:

package_show(id="covid-19-data")

organization_list

List all organizations.

organization_show

Get details about a specific organization.

Parameters:

  • id (string, required): Organization ID or name

resource_search

Search for resources within datasets.

Parameters:

  • query (string): SOLR query (e.g., "format:CSV")

  • order_by (string): Sort field

  • offset (int): Pagination offset

  • limit (int): Maximum results (default: 100)

datastore_search

Search data within a datastore resource.

Parameters:

  • resource_id (string, required): Resource ID

  • q (string): Full-text query

  • limit (int): Max records (default: 100)

  • offset (int): Pagination offset

  • sort (string): Sort order

  • fields (string): Comma-separated field names

  • Other CKAN datastore parameters

Example:

datastore_search( resource_id="abc123", q="Tel Aviv", limit=50, sort="date desc" )

fetch_data

Convenience tool to get data from a dataset by name.

Automatically finds the first resource and fetches its data.

Parameters:

  • dataset_name (string, required): Dataset name or ID

  • limit (int): Number of records (default: 100)

  • offset (int): Pagination offset

Visualization Tools 📊

dataset_profile

Profile a dataset to understand its structure and quality.

Parameters:

  • resource_id (string, required): Resource ID to profile

  • sample_size (int): Number of records to analyze (default: 100)

Returns:

  • Field types (integer, number, string, coordinate)

  • Missing value statistics

  • Numeric statistics (min, max, mean)

  • Top values for categorical fields

Example:

dataset_profile(resource_id="abc123", sample_size=500)

Response:

{ "resource_id": "abc123", "sample_size": 500, "total_fields": 5, "fields": [ { "name": "age", "type": "integer", "stats": { "count": 500, "null_count": 5, "min": 18, "max": 95, "mean": 42.3 }, "missingness": 0.01 }, { "name": "city", "type": "string", "stats": { "count": 500, "null_count": 2, "unique_count": 12, "top_values": { "Tel Aviv": 150, "Jerusalem": 120, "Haifa": 80 } }, "missingness": 0.004 } ] }

chart_generator

Generate interactive Vega-Lite charts.

Parameters:

  • resource_id (string, required): Resource ID

  • chart_type (string, required): 'histogram', 'bar', 'line', or 'scatter'

  • x_field (string, required): X-axis field name

  • y_field (string): Y-axis field name (not needed for histogram)

  • title (string): Chart title

  • limit (int): Max records to visualize (default: 100)

Returns:

  • vega_lite_spec: Vega-Lite JSON specification

  • html: Self-contained HTML with embedded chart

Example:

# Create a histogram chart_generator( resource_id="abc123", chart_type="histogram", x_field="age", title="Age Distribution" ) # Create a bar chart chart_generator( resource_id="abc123", chart_type="bar", x_field="city", y_field="population", title="Population by City" )

map_generator

Generate interactive maps from geographic data.

Parameters:

  • resource_id (string, required): Resource ID

  • lat_field (string, required): Latitude field name

  • lon_field (string, required): Longitude field name

  • limit (int): Max points to map (default: 500)

Returns:

  • geojson: GeoJSON FeatureCollection

  • html: Interactive Leaflet map

  • point_count: Number of valid points

  • center: Map center coordinates

Example:

map_generator( resource_id="abc123", lat_field="latitude", lon_field="longitude", limit=1000 )

Usage Examples

Finding and Visualizing Data

# 1. Search for datasets results = package_search(q="traffic accidents", rows=5) # 2. Get dataset details dataset = package_show(id=results["result"]["results"][0]["name"]) # 3. Get the resource ID resource_id = dataset["result"]["resources"][0]["id"] # 4. Profile the data profile = dataset_profile(resource_id=resource_id) # 5. Generate a map if geographic data exists if any(f["type"] == "coordinate" for f in profile["fields"]): map_data = map_generator( resource_id=resource_id, lat_field="latitude", lon_field="longitude" ) # Save the HTML map with open("map.html", "w") as f: f.write(map_data["html"])

Creating Visualizations

# Get COVID-19 data data = fetch_data(dataset_name="covid-19-cases") # Profile to understand the data profile = dataset_profile(resource_id=data["resource_id"]) # Create a line chart of cases over time chart = chart_generator( resource_id=data["resource_id"], chart_type="line", x_field="date", y_field="new_cases", title="COVID-19 New Cases Over Time" ) # Save the chart with open("chart.html", "w") as f: f.write(chart["html"])

Development

Running Tests

# Run all tests pytest tests/ -v # Run specific test file pytest tests/test_tools.py -v # Run with coverage pytest tests/ --cov=datagov_mcp

Code Style

# Check code ruff check . # Format code ruff format .

Project Structure

datagov-mcp/ ├── datagov_mcp/ # Main package │ ├── __init__.py │ ├── server.py # Core CKAN tools │ ├── api.py # CKAN API helper │ ├── client.py # HTTP client │ └── visualization.py # Visualization tools ├── tests/ # Test suite (34 tests) │ ├── test_api.py │ ├── test_contracts.py │ ├── test_tools.py │ └── test_visualization.py ├── server.py # Entrypoint └── pyproject.toml # Dependencies

Architecture

Async HTTP Layer

All HTTP requests use httpx.AsyncClient with:

  • 30-second timeout

  • Automatic retries for 5xx errors

  • Proper error handling and logging

  • Connection pooling

Error Handling

from datagov_mcp.api import CKANAPIError try: result = await ckan_api_call("package_show", params={"id": "test"}) except CKANAPIError as e: print(f"Error: {e.message}") print(f"Status code: {e.status_code}")

Release Notes

See CHANGELOG.md for detailed release notes.

Latest: v0.3.0 (2024-02-14)

Major modernization release:

  • ✅ Migrated to async httpx (from blocking requests)

  • ✅ Added 3 visualization tools (profile, charts, maps)

  • ✅ Comprehensive test suite (34 tests, 100% pass rate)

  • ✅ CI/CD with GitHub Actions

  • ✅ Package structure with proper modules

  • ✅ Detailed documentation and examples

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick start:

  1. Fork the repository

  2. Create a feature branch

  3. Make changes with tests

  4. Submit a pull request

For examples of other MCP servers, see the Model Context Protocol servers repository.

Troubleshooting

Port Conflicts

If you encounter port conflicts with MCP Inspector:

# Install nano-dev-utils pip install nano-dev-utils # Release ports python -c "from nano_dev_utils import release_ports; release_ports.PortsRelease().release_all()"

Windows + OneDrive

For Windows users: avoid running installation in folders watched by OneDrive. See this issue for details.

Import Errors

If you encounter import errors, ensure you installed in editable mode:

pip install -e ".[dev]"

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

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