Statistics Canada MCP Server

Statistics Canada MCP Server

[](https://safeskill.dev/scan/aryan-jhaveri-mcp-statcan)

MCP server and CLI for Statistics Canada's Web Data Service (WDS) and SDMX REST API. Gives any MCP client — Claude, Cursor, VS Code Copilot, Gemini — structured access to Canadian statistical data. Includes a standalone statcan CLI for direct downloads without an LLM.

> Hosted on Render — no install required for most users. See [Quick Start](#quick-start).

⚠️ LLMs may fabricate data. Always verify important figures against official Statistics Canada sources.

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Table of Contents

• [Quick Start](#quick-start)
• [Examples](#examples)
• [Setup by Client](#setup-by-client)
• [How Claude.ai Uses This Server](#how-claudeai-uses-this-server)
• [MCP Prompts](#mcp-prompts)
• [statcan CLI](#statcan-cli)
• [Features & Tools](#features--tools)
• [Project Structure](#project-structure)
• [Known Issues](#known-issues)

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Quick Start

Pick the option that fits you. You don't need to install anything for Option 1.

Option 1 — Use the hosted server (recommended)

Connect directly to the public server on Render. No uv, no terminal, no local setup.

Claude Desktop / Claude.ai

1. Open Settings → Connectors → Add Custom Connector
2. Name: mcp-statcan
3. URL: https://mcp-statcan.onrender.com/mcp
4. Save and restart

Claude Code

claude mcp add statcan --transport http https://mcp-statcan.onrender.com/mcp --scope global

> The hosted server provides all WDS + SDMX tools. Database tools (SQLite) require local setup (Option 3) — they are intentionally excluded from the shared server.

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Option 2 — Self-host HTTP (WDS + SDMX, no DB)

Run a local server with the same tools as the hosted version.

Step 1 — Install uv:

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Step 2 — Start the server:

uvx statcan-mcp-server --transport http
# Listening at http://localhost:8000

Step 3 — Connect your client to http://localhost:8000/mcp.

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Option 3 — Full local setup (WDS + SDMX + SQLite)

Everything from Option 2, plus database tools for storing and querying data with SQL. Runs via stdio.

Step 1 — Install uv (same as above).

Step 2 — Configure your client with the stdio snippets in [Setup by Client](#setup-by-client) below.

uvx downloads and runs the server automatically on first use.

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Option 4 — statcan CLI (no LLM needed)

Download StatCan data directly from the terminal. See [statcan CLI](#statcan-cli).

uvx statcan-mcp-server        # installs the package
statcan search "labour force"
statcan download 14-10-0287-01 --last 12 --output lfs.csv

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Examples

Chat examples

| Dataset | Query | Demo | Source | |---|---|---|---| | Canada's Greenhouse Gas Emissions | "Create a simple visualization for greenhouse emissions for Canada as a whole over the last 4 years" | Chat | Table 38-10-0097-01 | | Canada's International Trade in Services | "Create a quick analysis for international trade in services for the last 6 months with a visualization" | Chat | Table 12-10-0144-01 | | Ontario Building Construction Price Index | "Generate a visualization for Ontario's Building Price index from Q4 2023 to Q4 2024" | Chat | Table 18-10-0289-01 | | Canadian Unemployment Dashboard | "Create a Canadian Unemployment Dashboard using statcan mcp" | Chat | Table 14-10-0287-01 |

Dashboard examples

| Title | Link | Source | |---|---|---| | Canada's Critical Minerals Economy | Dashboard | Table 36-10-0708-01 | | Price of Everything: CPI Dashboard 2015–2026 | Dashboard | Table 18-10-0004-01 | | Canada's Biomedical & Biotech Industries | Dashboard | Table 27-10-0297-01 |

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Setup by Client

Hosted server (Option 1)

Claude Desktop — Settings → Connectors → Add Custom Connector

• Name: mcp-statcan
• URL: https://mcp-statcan.onrender.com/mcp

Claude Code

claude mcp add statcan --transport http https://mcp-statcan.onrender.com/mcp --scope global

Cursor — .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):

{
  "mcpServers": {
    "statcan": {
      "url": "https://mcp-statcan.onrender.com/mcp"
    }
  }
}

VS Code (GitHub Copilot) — .vscode/mcp.json:

{
  "servers": {
    "statcan": {
      "type": "http",
      "url": "https://mcp-statcan.onrender.com/mcp"
    }
  }
}

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Self-hosted HTTP (Option 2)

> Start uvx statcan-mcp-server --transport http first, then configure your client.

Most clients need mcp-proxy to bridge stdio ↔ HTTP. Claude Code connects natively.

Claude Desktop — Settings → Developer → Edit Config:

{
  "mcpServers": {
    "statcan": {
      "command": "uvx",
      "args": ["mcp-proxy", "--transport", "streamablehttp", "http://localhost:8000/mcp"]
    }
  }
}

Claude Code

claude mcp add statcan --transport http http://localhost:8000/mcp --scope global

Cursor / VS Code / Gemini — same mcp-proxy wrapper, pointing to http://localhost:8000/mcp.

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Full local / stdio (Option 3)

Claude Desktop — Settings → Developer → Edit Config:

{
  "mcpServers": {
    "statcan": {
      "command": "uvx",
      "args": ["statcan-mcp-server", "--db-path", "/Users//.statcan-mcp/statcan_data.db"]
    }
  }
}

> Pass --db-path with an absolute path. Claude Desktop overrides the subprocess HOME env var, which can break default path resolution.

Claude Code

claude mcp add statcan --scope global -- uvx statcan-mcp-server

Cursor / VS Code / Gemini — use uvx statcan-mcp-server as the stdio command.

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How Claude.ai Uses This Server

Claude.ai (web) has no bash sandbox — it can't run shell commands. Instead, it uses MCP tools for discovery and its Python script tool to fetch data without bloating the context window.

The pattern:

1. MCP tools (small payloads — metadata only):
   search_cubes_by_title("labour force")  → productId
   get_sdmx_structure(productId=...)      → dimension layout + codes
   get_sdmx_key_for_dimension(...)        → OR key for large dimensions

2. Python script (data never enters context):
   url = "https://mcp-statcan.onrender.com/files/sdmx//?lastNObservations=12"
   → validate URL domain → write to ./statcan_.csv → print summary only

3. Follow-up script (analysis from local file):
   rows = list(csv.DictReader(open("./statcan_.csv")))
   → filter / sort / aggregate → print only the result

get_sdmx_data on the hosted server always returns a download_csv URL instead of inline data — data stays out of the context window regardless of response size.

Claude Code (bash sandbox) uses the statcan CLI instead:

statcan search "labour force"
statcan download 14-10-0287-01 --last 12 --output ./lfs.csv
awk -F',' 'NR>1 && $1=="Canada"' ./lfs.csv | sort -t',' -rn -k5 | head -10

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MCP Prompts

The server ships five prompts accessible as slash commands in supported clients. Each has dual instructions — Claude Code (bash) and Claude.ai web (Python script).

| Prompt | What it teaches | |---|---| | /statcan-data-lookup | End-to-end: search → structure → build key → fetch to local file → analyze | | /sdmx-key-builder | SDMX key syntax: wildcards, OR keys, time parameters, download URL format | | /statcan-download | Download a specific table: CLI commands + Python script alternative | | /statcan-explore | Sample before committing: 3-period fetch, column layout, size estimate | | /statcan-vector-pipeline | Multi-series download and cross-series comparison |

Usage in Claude Code:

/statcan-data-lookup topic="consumer price index" analysis_goal="trend last 5 years"
/statcan-download product_id=18100004 last_n=24

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statcan CLI

A standalone CLI for downloading StatCan data without an LLM. Outputs pipe-friendly CSV/JSON to stdout; progress and errors go to stderr.

Install:

pip install statcan-mcp-server   # or: uvx statcan-mcp-server (no install)

Commands:

statcan search             Search tables by keyword
statcan metadata     Show table structure (dimensions + members)
statcan download     Download observations via SDMX
statcan vector ...    Download one or more vector series
statcan codeset                  Show StatCan code definitions (UOM, frequency, etc.)

Common usage:

# Find a table
statcan search "consumer price index"
statcan search "labour force" --max-results 10 --format json

# Inspect structure before downloading
statcan metadata 18-10-0004-01
statcan metadata 18100004 --full        # show all dimension members

# Download data
statcan download 18-10-0004-01 --last 12 --output cpi.csv
statcan download 18-10-0004-01 --key "1.1.1" --start 2020-01 --end 2024-12
statcan download 18-10-0004-01 --last 5 --dry-run   # preview SDMX URL

# Download by vector ID
statcan vector v41690973 --last 24 --output series.csv
statcan vector v41690973 v41690974 --last 12 --output multi.csv

# Decode numeric codes
statcan codeset --type uom
statcan codeset --type frequency --format json

Output formats: csv (default for download/vector), table (default for search/metadata/codeset), json

Pipe patterns:

# Top 10 by value
statcan download 14-10-0287-01 --last 1 --format csv \
  | awk -F',' 'NR>1' | sort -t',' -k5 -rn | head -10

# Extract unique geographies
statcan download 14-10-0287-01 --last 1 --format csv \
  | awk -F',' 'NR>1 {print $1}' | sort -u

# Chain search → download
PID=$(statcan search "CPI" --format json | python3 -c "import sys,json; print(json.load(sys.stdin)[0]['Product ID'])")
statcan download $PID --last 12 --output cpi.csv

For the complete CLI reference see [cli.md](cli.md).

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Features & Tools

SDMX Tools — server-side filtered data fetch

Only the slice you request is returned. No downloading full tables.

| Tool | Description | |---|---| | get_sdmx_structure | Dimension codelists + key syntax for a table. Call before get_sdmx_data. | | get_sdmx_data | Filtered observations by productId + key. Returns a CSV download URL on the hosted server — data stays out of context. | | get_sdmx_vector_data | Observations for a single vectorId via SDMX. | | get_sdmx_key_for_dimension | All leaf member IDs for a large dimension as a ready-to-paste OR key. Use when a dimension has >30 codes (e.g. NOC, CMAs). |

Key syntax (passed to get_sdmx_data):

• "1.2.1" — Geography=1, Gender=2, Age=1
• ".2.1" — all geographies (wildcard), Gender=2, Age=1
• "1+2.2.1" — Geography 1 or 2, Gender=2, Age=1

> Note: Wildcard (.) on dimensions with >30 codes returns a sparse, unpredictable sample. Use get_sdmx_key_for_dimension to get the correct OR key.

WDS Discovery & Metadata

| Tool | Description | |---|---| | search_cubes_by_title | Full-text search across all StatCan tables. AND logic, capped at 25 results. | | get_all_cubes_list / _lite | Paginated table inventory (offset/limit, default 100/page). | | get_cube_metadata | Dimension info, member lists, date ranges. summary=True caps members at 10 per dimension. | | get_code_sets | Decode StatCan numeric codes (frequency, UOM, scalar factor, status). |

WDS Series Resolution & Change Detection

| Tool | Description | |---|---| | get_series_info | Resolve {productId, coordinate} pairs to vectorId + metadata. | | get_series_info_from_vector | Resolve a vectorId to productId, coordinate, titles, frequency. | | get_changed_cube_list | Tables updated on a specific date. | | get_changed_series_list | Series updated on a specific date. | | get_changed_series_data_from_cube_pid_coord | Data points that changed for a coordinate. | | get_changed_series_data_from_vector | Data points that changed for a vectorId. | | get_bulk_vector_data_by_range | Multiple vectors filtered by release date range. |

Composite & Database Tools (local/stdio mode only)

These tools are not available on the hosted Render server — SQLite is per-process and not shared across users.

| Tool | Description | |---|---| | fetch_vectors_to_database | Fetch vectors by reference period range and store to SQLite. | | store_cube_metadata | Fetch full cube metadata into SQLite — browse all members and vectorIds with SQL. | | query_database | Read-only SQL against the local SQLite database. | | create_table_from_data / insert_data | Create or append to a table. | | list_tables / get_table_schema / drop_table | Database utilities. |

Typical workflow

Claude.ai web (hosted server):

1. search_cubes_by_title("unemployment rate")
   → productId e.g. 14100287

2. get_sdmx_structure(productId=14100287)
   → dimension positions + sample codes

3. get_sdmx_key_for_dimension(productId=14100287, dimension_position=3)
   → or_key for large dimensions

4. get_sdmx_data(productId=14100287, key=".2.1", lastNObservations=24)
   → returns download_csv URL

5. Python script: validate URL domain → write to ./statcan_14100287.csv → analyze → print summary

Claude Code (bash sandbox):

statcan search "unemployment rate"
statcan metadata 14100287
statcan download 14-10-0287-01 --last 24 --output ./lfs.csv
awk -F',' 'NR>1 && $1=="Canada"' ./lfs.csv | sort -t',' -rn -k5 | head -10

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Project Structure

src/
├── api/
│   ├── cube/
│   │   ├── discovery.py         # search_cubes_by_title, get_all_cubes_list
│   │   ├── metadata.py          # get_cube_metadata
│   │   └── series.py            # get_series_info, change detection
│   ├── vector/
│   │   └── vector_tools.py      # vector series, bulk range fetch
│   ├── sdmx/
│   │   └── sdmx_tools.py        # get_sdmx_structure, get_sdmx_data, get_sdmx_key_for_dimension
│   ├── composite_tools.py       # fetch_vectors_to_database, store_cube_metadata (stdio only)
│   └── metadata_tools.py        # get_code_sets
├── cli/
│   ├── main.py                  # statcan CLI entry point (Typer app)
│   ├── output.py                # write_output, format helpers
│   └── commands/
│       ├── search.py            # statcan search
│       ├── metadata.py          # statcan metadata
│       ├── download.py          # statcan download
│       ├── vector.py            # statcan vector
│       └── codeset.py           # statcan codeset
├── db/                          # SQLite connection, schema, queries (stdio only)
├── models/                      # Pydantic input models
├── util/
│   ├── registry.py              # ToolRegistry — @decorator → MCP Tool schema
│   ├── truncation.py            # Response truncation + pagination guidance
│   ├── sdmx_json.py             # SDMX-JSON → tabular rows
│   └── cache.py                 # 1-hour TTL cache for cube list
├── config.py                    # BASE_URL, SDMX_BASE_URL, RENDER_BASE_URL, TRANSPORT, PORT
└── server.py                    # create_server(), MCP Prompts, HTTP routes (/files/sdmx/), CLI

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Known Issues

| Issue | Status | Workaround | |---|---|---| | "Unable to open database file" on Claude Desktop | Active | Pass --db-path /Users//.statcan-mcp/statcan_data.db in your config | | SSL verification disabled | Active | VERIFY_SSL = False in all API calls — StatCan cert issues made this necessary | | lastNObservations + startPeriod/endPeriod → 406 | Active | Use one or the other, not both | | OR syntax for Geography dimension unreliable | Active | Use wildcard (.) for Geography; OR works fine for other dimensions | | Wildcard returns sparse data for large dimensions | Mitigated | Use get_sdmx_key_for_dimension to get the full

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Aryan-Jhaveri
• Source: Aryan-Jhaveri/mcp-statcan
• License: MIT
• Homepage: https://mcp-statcan.onrender.com

Install and usage instructions live in the source repository linked above.