Statistics Canada API MCP Server

Overview

The Mcp Statcan server is a specialized Model Context Protocol (MCP) endpoint that bridges large language models with the rich data ecosystem of Statistics Canada. By exposing StatCan’s Web Data Service through a set of well‑defined MCP tools, it allows AI assistants to query, filter, and visualize Canadian statistical datasets without writing custom API calls or parsing raw JSON responses. This eliminates a common pain point for developers who need authoritative, up‑to‑date data in conversational AI workflows.

The server is built on the FastMCP framework, which provides a lightweight yet powerful foundation for defining resources, prompts, and sampling strategies. Internally it uses to communicate with StatCan’s REST endpoints, handling authentication, pagination, and error mapping automatically. When an MCP client sends a request—such as “retrieve greenhouse gas emissions for Canada from 2018 to 2022”—the server translates that into the appropriate StatCan API call, fetches the data, and returns it in a structured JSON payload. The client can then feed this payload back into the LLM for summarization, chart generation, or further analysis.

Key capabilities include:

• Cube Operations: List available data cubes, retrieve metadata, and query specific dimensions (geography, time, variable) in a single call.
• Table Retrieval: Pull entire tables or filtered subsets using intuitive filter syntax, making it easy to isolate the exact rows and columns needed.
• Structured Responses: All data is returned in a machine‑readable format, ready for downstream processing or visualisation libraries.
• Prompt Integration: The server can supply example prompts and best‑practice guidelines to help developers craft queries that yield accurate results.

Typical use cases span public policy analysis, academic research, and business intelligence. For instance, a developer building a climate‑impact dashboard can let the AI assistant automatically fetch the latest emissions data, validate it against official sources, and generate a clean chart—all within a conversational interface. Similarly, trade analysts can pull recent service‑trade figures and compare them across provinces without leaving the chat.

Because MCP standardises the interaction model, any LLM that supports the protocol (Claude, GPT‑4o, etc.) can immediately leverage StatCan data. This plug‑and‑play nature means teams can augment their AI workflows with authoritative Canadian statistics in minutes, rather than months of custom integration work. The result is a seamless, trustworthy data pipeline that empowers developers to build richer, evidence‑based AI applications.