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

# 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"

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

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

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

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

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

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

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

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

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

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/<pid>/<key>?lastNObservations=12"
   → validate URL domain → write to ./statcan_<pid>.csv → print summary only

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

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

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

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

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

# 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

# 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

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

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

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