mcp-open-data-hk 0.1.3

MCP server for accessing data.gov.hk open data

mcp-open-data-hk

This is an MCP (Model Context Protocol) server that provides access to data from data.gov.hk, the official open data portal of the Hong Kong government.

Installation

You can install the package directly from PyPI:

pip install mcp-open-data-hk

After installation, configure your MCP-compatible client (like Cursor, Claude Code, or Claude Desktop) by adding the following to your settings.json:

{
  "mcpServers": {
    "mcp-open-data-hk": {
      "command": "python",
      "args": ["-m", "mcp_open_data_hk"]
    }
  }
}

Features

The server provides the following tools to interact with the data.gov.hk API:

1. list_datasets - Get a list of dataset IDs

2. get_dataset_details - Get detailed information about a specific dataset

3. list_categories - Get a list of data categories

4. get_category_details - Get detailed information about a specific category

5. search_datasets - Search for datasets by query term with advanced options

6. search_datasets_with_facets - Search datasets and return faceted results

7. get_datasets_by_format - Get datasets by file format

8. get_supported_formats - Get list of supported file formats

9. For local development:

   python src/server.py
   

10. Using FastMCP CLI:

    fastmcp run src/server.py:mcp
    

Tools

list_datasets

Get a list of dataset IDs from data.gov.hk

Parameters:

• limit (optional): Maximum number of datasets to return (default: 1000)
• offset (optional): Offset of the first dataset to return
• language (optional): Language code (en, tc, sc) - defaults to "en"

get_dataset_details

Get detailed information about a specific dataset

Parameters:

• dataset_id: The ID or name of the dataset to retrieve
• language (optional): Language code (en, tc, sc) - defaults to "en"
• include_tracking (optional): Add tracking information to dataset and resources - defaults to False

list_categories

Get a list of data categories (groups)

Parameters:

• order_by (optional): Field to sort by ('name' or 'packages') - deprecated, use sort instead
• sort (optional): Sorting of results ('name asc', 'package_count desc', etc.) - defaults to "title asc"
• limit (optional): Maximum number of categories to return
• offset (optional): Offset for pagination
• all_fields (optional): Return full group dictionaries instead of just names - defaults to False
• language (optional): Language code (en, tc, sc) - defaults to "en"

get_category_details

Get detailed information about a specific category (group)

Parameters:

• category_id: The ID or name of the category to retrieve
• include_datasets (optional): Include a truncated list of the category's datasets - defaults to False
• include_dataset_count (optional): Include the full package count - defaults to True
• include_extras (optional): Include the category's extra fields - defaults to True
• include_users (optional): Include the category's users - defaults to True
• include_groups (optional): Include the category's sub groups - defaults to True
• include_tags (optional): Include the category's tags - defaults to True
• include_followers (optional): Include the category's number of followers - defaults to True
• language (optional): Language code (en, tc, sc) - defaults to "en"

search_datasets

Search for datasets by query term using the package_search API.

This function searches across dataset titles, descriptions, and other metadata to find datasets matching the query term. It supports advanced Solr search parameters.

Parameters:

• query (optional): The solr query string (e.g., "transport", "weather", ":" for all) - defaults to ":"
• limit (optional): Maximum number of datasets to return (default: 10, max: 1000)
• offset (optional): Offset for pagination - defaults to 0
• language (optional): Language code (en, tc, sc) - defaults to "en"

Returns: A dictionary containing:

• count: Total number of matching datasets
• results: List of matching datasets (up to limit)
• search_facets: Faceted information about the results
• has_more: Boolean indicating if there are more results available

search_datasets_with_facets

Search for datasets and return faceted results for better data exploration.

This function is useful for exploring what types of data are available by showing counts of datasets grouped by tags, organizations, or other facets.

Parameters:

• query (optional): The solr query string - defaults to ":"
• language (optional): Language code (en, tc, sc) - defaults to "en"

Returns: A dictionary containing:

• count: Total number of matching datasets
• search_facets: Faceted information about the results
• sample_results: First 3 matching datasets

get_datasets_by_format

Get datasets that have resources in a specific file format.

Parameters:

• file_format: The file format to filter by (e.g., "CSV", "JSON", "GeoJSON")
• limit (optional): Maximum number of datasets to return - defaults to 10
• language (optional): Language code (en, tc, sc) - defaults to "en"

Returns: A dictionary containing:

• count: Total number of matching datasets
• results: List of matching datasets

get_supported_formats

Get a list of file formats supported by data.gov.hk

Returns: A list of supported file formats

Local Testing

Run test scripts:

python tests/test_client.py
python tests/debug_search.py
python tests/comprehensive_test.py

Run server directly:

python src/server.py

Run with FastMCP CLI:

fastmcp run src/server.py:mcp

Run unit tests:

pytest tests/

Adding to AI Assistants

For Cursor:

Using FastMCP CLI (recommended):

fastmcp install cursor src/server.py --server-name "mcp-open-data-hk"

Manual Configuration via UI:

1. Open Cursor Settings
2. Go to MCP Servers
3. Click "Add Server"
4. Enter:
   • Name: mcp-open-data-hk
   • Command: python
   • Arguments: ["-m", "mcp_open_data_hk"]
   • Working Directory: /full/path/to/mcp-open-data-hk

Manual Configuration via settings.json: Edit your Cursor settings.json file and add:

{
  "mcpServers": {
    "mcp-open-data-hk": {
      "command": "python",
      "args": ["-m", "mcp_open_data_hk"],
      "cwd": "/full/path/to/mcp-open-data-hk"
    }
  }
}

For Claude Code:

Using FastMCP CLI (recommended):

fastmcp install claude-code src/server.py --server-name "mcp-open-data-hk"

Manual Configuration via UI:

1. Open Claude Code Settings
2. Go to MCP Servers
3. Click "Add Server"
4. Enter:
   • Name: mcp-open-data-hk
   • Command: python
   • Arguments: ["-m", "mcp_open_data_hk"]
   • Working Directory: /full/path/to/mcp-open-data-hk

Manual Configuration via settings.json: Edit your Claude Code settings.json file and add:

{
  "mcpServers": {
    "mcp-open-data-hk": {
      "command": "python",
      "args": ["-m", "mcp_open_data_hk"],
      "cwd": "/full/path/to/mcp-open-data-hk"
    }
  }
}

For Claude Desktop:

Using FastMCP CLI (recommended):

fastmcp install claude-desktop src/server.py --server-name "mcp-open-data-hk"

Understanding Path Configuration

When installed as a package, the server can be referenced by its module name rather than file path. This is more convenient for users as they don't need to specify full file paths.

Installed Package:

{
  "mcpServers": {
    "mcp-open-data-hk": {
      "command": "python",
      "args": ["-m", "mcp_open_data_hk"]
    }
  }
}

Local Development (file path approach):

{
  "mcpServers": {
    "mcp-open-data-hk": {
      "command": "python",
      "args": ["/full/path/to/mcp-open-data-hk/src/server.py"],
      "cwd": "/full/path/to/mcp-open-data-hk"
    }
  }
}

The package installation approach is recommended for end users, while the file path approach is useful for local development and testing.

Alternative Approach: Packaging for Distribution

If you want to make your server work like the published examples, you can package it for distribution:

1. Package as a Python package and publish it to PyPI
2. Users would then install and run it like:

   pip install mcp-open-data-hk
   mcp-open-data-hk
   

However, for local development and testing, you can run it directly as shown in the Installation section.

Using Environment Variables

Create a .env file in the project root:

DEBUG=true
TIMEOUT=30

Then install with:

# For Cursor
fastmcp install cursor src/server.py --server-name "mcp-open-data-hk" --env-file .env

# For Claude Code
fastmcp install claude-code src/server.py --server-name "mcp-open-data-hk" --env-file .env

Or add to settings.json:

{
  "mcpServers": {
    "mcp-open-data-hk": {
      "command": "python",
      "args": ["/full/path/to/data_gov_hk_mcp/src/server.py"],
      "cwd": "/full/path/to/data_gov_hk_mcp",
      "env": {
        "DEBUG": "true",
        "TIMEOUT": "30"
      }
    }
  }
}

Example Queries

Once installed, try these queries with your AI assistant:

1. "List some datasets from the Hong Kong government data portal"
2. "Find datasets related to transportation in Hong Kong"
3. "What categories of data are available on data.gov.hk?"
4. "Get details about the flight information dataset"
5. "Search for datasets about weather in Hong Kong"
6. "What file formats are supported by data.gov.hk?"
7. "Find CSV datasets about population"
8. "Show me the most common tags in transport datasets"

The AI will automatically use the appropriate tools from your MCP server to fetch the requested information.

Troubleshooting

Common Issues

1. Module not found errors: Make sure you've installed the dependencies with pip install -r requirements.txt

2. Path issues: Ensure the paths in your IDE configuration are correct absolute paths

3. Permission errors: On Unix systems, make sure the scripts have execute permissions:

   chmod +x src/server.py
   

4. FastMCP not found: Install it with:

   pip install fastmcp
   

Testing the Connection

If you're having issues, you can test the connection manually:

1. Run the server in one terminal:

   python src/server.py
   

2. In another terminal, run the test client:

   python tests/test_client.py
   

If this works, the issue is likely in the IDE configuration.

Extending the Server

You can extend the server by adding more tools in src/server.py. Follow the existing patterns:

1. Add a new function decorated with @mcp.tool
2. Provide a clear docstring explaining the function and parameters
3. Implement the functionality
4. Test with the client

The server automatically exposes all functions decorated with @mcp.tool to MCP clients.

GitHub Workflows

This project includes GitHub Actions workflows for CI/CD:

1. CI Workflow: Runs tests across multiple Python versions (3.10-3.12) on every push/PR to main branch
2. Publish Workflow: Automatically builds and publishes to TestPyPI on every push to main, and to PyPI on version tags (v*..)
3. Code Quality Workflow: Checks code formatting and linting on every push/PR
4. Release Workflow: Automatically creates GitHub releases when tags are pushed

Setup for Publishing (Trusted Publishing)

This project uses PyPI's Trusted Publishing which is more secure than using API tokens. To set it up:

1. Go to https://pypi.org/manage/account/publishing/ and add a new pending publisher with:

   • Project name: mcp-open-data-hk
   • Owner: Your GitHub username or organization
   • Repository name: mcp-open-data-hk
   • Workflow name: publish.yml
   • Environment name: pypi

2. Go to https://test.pypi.org/manage/account/publishing/ and add a new pending publisher with the same information but use testpypi as the environment name.

3. In your GitHub repository, go to "Settings" > "Environments" and create two environments:

   • pypi - Set "Required reviewers" to your username for security
   • testpypi - No additional configuration needed

With Trusted Publishing, no API tokens need to be created or stored as secrets.

GitHub Environments

For the Trusted Publishing to work correctly, you need to create two environments in your GitHub repository settings:

1. pypi - This environment requires manual approval for security when publishing to PyPI
2. testpypi - This environment doesn't require manual approval and will automatically publish to TestPyPI

To create these environments:

1. Go to your repository's "Settings" tab
2. Click on "Environments" in the left sidebar
3. Click "New environment"
4. Create the pypi environment and enable "Required reviewers" with your username
5. Create the testpypi environment with no additional settings

Releasing New Versions

To release a new version:

1. Update the version number in pyproject.toml
2. Commit the changes
3. Create and push a new tag:

   git tag -a v1.0.0 -m "Release version 1.0.0"
   git push origin v1.0.0
   

Or use the provided release script:

./release.sh 1.0.0

This will automatically trigger the publish workflow to build and publish the package to TestPyPI and PyPI (for tagged releases), and create a GitHub release.

Contributing

Contributions are welcome! Please read our Contributing Guide and Code of Conduct for details on how to contribute to this project.

Project Structure

mcp-open-data-hk/
├── src/
│   └── server.py          # Main MCP server implementation
├── tests/
│   ├── test_client.py     # Client test script
│   ├── debug_search.py    # Search functionality test
│   ├── comprehensive_test.py # Comprehensive functionality test
│   └── test_data_gov_hk.py # Unit tests
├── requirements.txt       # Python dependencies
├── pyproject.toml         # Project configuration
├── README.md             # This file
├── run_examples.sh       # Example commands script
├── install.sh            # Installation helper script
├── release.sh            # Release helper script
└── .gitignore            # Git ignore file

License

This project is licensed under the MIT License.