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.

Screenshots

Visualization Tools in Action

Generate interactive charts and maps from Israeli government datasets:

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

• Built with FastMCP

• Data from Israel Open Data Portal

• Charts powered by Vega-Lite

• Maps powered by Leaflet