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Toronto Open Data MCP Server

@vduquette

Repository

Basic information

Category

Data & Analytics

License

MIT license

Transports

stdio

Author

vduquette

About Toronto Open Data MCP Server

No overview available yet

#toronto-open-data#data-query#open-data

Toronto Open Data MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "toronto-open-data-mcp-server": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

Toronto Open Data MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Toronto Open Data MCP Server?

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

How to use Toronto Open Data MCP Server?

Clone the repository, install dependencies with pip install -e ., then run python main.py. The primary tool is toronto_find_and_query_data(user_question), a one-step solution that finds and processes the most relevant data. Alternative tools include toronto_start_here(), toronto_search_datasets(query), and toronto_smart_data_helper(dataset_id, user_question).

Key features of Toronto Open Data MCP Server

  • Intelligent Query Engine that automatically finds and returns relevant data.
  • Relevance scoring to rank datasets by question relevance.
  • Smart filtering based on context (recent data, failures, locations).
  • Adaptive data processing for real‑time API and CSV data.
  • Agent‑optimized design for minimal LLM decision complexity.
  • Robust deployment ready with health checks for Railway/cloud.

Use cases of Toronto Open Data MCP Server

  • Ask natural language questions like “What

Comments

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