Toronto Open Data MCP Server

An MCP (Model Context Protocol) server that provides direct access to Toronto's Open Data through the CKAN API. This server allows LLM agents to efficiently discover, explore, and query Toronto's 500+ public datasets.

Features

• 🔍 Smart Dataset Discovery: Search through 500+ Toronto datasets with intelligent suggestions
• 🧠 Intelligent Data Helper: Automatically handles both API and CSV data sources
• 📊 Flexible Querying: Support for filtering, sorting, and field selection on API datasets
• 📁 CSV Support: Automatic fetching and preview of downloadable CSV datasets
• 🤝 LLM-Friendly: Designed for collaborative use with web search when additional context is needed
• ✅ Robust Error Handling: Clear error messages with actionable suggestions

Installation

Option 1: Using uvx (Recommended for MCP)

The easiest way to use this server with any MCP client:

brew install uv

# No installation needed! uvx will handle everything
# Just use in your MCP client configuration:
uvx toronto-open-data-mcp-server

Option 2: Development Installation

1. Clone the repository:

   git clone https://github.com/yourusername/toronto-open-data-mcp-server.git
   cd toronto-open-data-mcp-server
   

2. Install dependencies:

   # Install main dependencies
   pip install -e .
   
   # Install test dependencies (optional)
   pip install -e ".[test]"
   

3. Run the server:

   python main.py
   

MCP Client Configuration

Using uvx (Easiest Method)

Add this to your MCP client configuration:

{
  "mcpServers": {
    "Toronto Open Data Server": {
      "command": "uvx",
      "args": ["toronto-open-data-mcp-server"]
    }
  }
}

Configuration File Locations

• Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json
• Cursor: Check Cursor's MCP documentation for config location
• Other MCP clients: Refer to your client's documentation

Usage

Quick Start

The server provides several tools designed for LLM agents:

1. toronto_start_here() - Essential first call that explains the workflow
2. toronto_search_datasets(query) - Find relevant datasets by keywords
3. toronto_smart_data_helper(dataset_id, user_question) - Intelligent data retrieval
4. toronto_popular_datasets() - Quick access to commonly used datasets

Example Workflow

# 1. Start with guidance
toronto_start_here()

# 2. Search for relevant data
toronto_search_datasets("restaurant inspection")

# 3. Get data intelligently
toronto_smart_data_helper("dinesafe", "recent restaurant inspection failures")

# 4. Advanced filtering (if needed)
toronto_query_dataset_data("dinesafe", 
                           filters={"establishment_status": "Conditional Pass"},
                           sort="inspection_date desc",
                           limit=10)

Popular Datasets

• dinesafe - Restaurant inspections and health scores
• traffic-signals - Traffic light locations and timing
• parks-facilities - Parks, pools, and recreation facilities
• business-licences - Licensed businesses in Toronto
• building-permits - Construction and renovation permits

Testing

This project includes comprehensive tests covering unit tests, integration tests, and workflow tests.

Prerequisites

Install test dependencies:

pip install -e ".[test]"

Running Tests

Quick Test Commands

# Run unit tests only (recommended for development)
python run_tests.py

# Run with verbose output
python run_tests.py --verbose

# Run with coverage report
python run_tests.py --coverage

# Run integration tests (hits real Toronto API)
python run_tests.py --integration

# Run all tests (unit + integration)
python run_tests.py --all

API Reference

Core Tools

toronto_start_here() -> str

Essential first call that provides workflow guidance and server capabilities.

toronto_search_datasets(query: str, limit: int = 10) -> str

Search Toronto datasets by keywords.

toronto_smart_data_helper(dataset_id: str, user_question: str, limit: int = 10) -> str

Intelligent helper that automatically handles both API and CSV data sources.

toronto_query_dataset_data(dataset_id: str, filters: Dict = None, fields: List = None, limit: int = 10, sort: str = None) -> str

Advanced querying with filtering and sorting for API datasets.

Utility Tools

toronto_popular_datasets() -> str

Quick access to commonly used datasets.

toronto_get_dataset_schema(dataset_id: str) -> str

Get field names and types for API datasets.

toronto_fetch_csv_data(csv_url: str, max_lines: int = 50) -> str

Fetch and preview CSV file content.

Architecture

• FastMCP Framework: Built on the FastMCP framework for easy tool definition
• CKAN API: Direct integration with Toronto's CKAN-based Open Data portal
• Collaborative Design: Works alongside web search rather than replacing it
• Error Recovery: Intelligent error handling with actionable suggestions

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Run the test suite: python run_tests.py --all
5. Submit a pull request