Un MCP pour les gouverner tous.

Les sept autres

• Statisticiens Le baccalauréat, province par province
• Démographes Ceux qui viennent s'établir : Edmonton et Calgary
• Urbanistes Le Canada construit en hauteur, pas en largeur
• Microéconomistes Le faible revenu d'une génération à l'autre
• Marketing Ce que coûte une carte à récompenses
• Scientifiques L'apprentissage automatique au bureau des brevets
• Analystes Le taux directeur depuis 2015

30

sources fédérales et nationales

56

sources provinciales, territoriales et municipales

12/13

provinces et territoires avec des données locales

215

outils derrière une seule connexion

Statistique Canada en détail Chaque tableau, série et fichier de microdonnées qu'un agent peut atteindre, et les difficultés que MapleStats règle pour vous. →

Voir chaque source et ce qu'elle couvreParcourir les 215 outils

1. 01

   Planifierplan_query

   Le planificateur lit la question et énumère les organismes à interroger, dans l'ordre, avec les précautions à prendre pour combiner leurs résultats. Les lieux qu'il reconnaît ajoutent leurs portails locaux. Le planificateur rédige pour l'instant son plan en anglais.

   Question

   Comment les loyers et les taux d'intérêt ont-ils évolué à Calgary depuis 2020 ?

   Plan

   Housing: starts, rents, prices, mortgages

   1. cmhc_list_categoriesCMHC starts, completions, rents and vacancy by centre
   2. cmhc_get_table_datapull the CMHC table for the place and period
   3. wds_search_cubesStatCan New Housing Price Index, building permits
   4. boc_search_seriesBank of Canada mortgage and policy rates
   5. statcan_census_profile_get_datacensus shelter cost and tenure by area

   Précaution CMHC reports by census metropolitan area and centre; StatCan price indexes are by CMA too, but the two define some areas differently, so name the geography each figure uses.

   Interest rates, exchange rates and markets

   1. boc_search_seriesfind the Valet series (e.g. FXUSDCAD, V39079)
   2. boc_get_observationspull the series for the period

   Calgary (ville)

   1. socrata_search_datasetssearch with portal='calgary'

2. 02

   Trouversearch_tools

   La recherche classe le catalogue selon une requête en langage courant et renvoie les 5 premiers outils. Le français fonctionne, car chaque outil porte des mots-clés français, même si sa description est en anglais. Le panneau exécute l'index du serveur dans votre navigateur : essayez votre propre requête.

   search_toolsS'exécute dans votre navigateur

   Décrivez les données recherchées

   >

   Essayez taux de chômage poids bootstrap microdonnées ronde d'invitations entrée express superficie brûlée feux de forêt federal contract awards

   1. 1boc_search_seriesBanque du CanadaSearch Bank of Canada Valet's ~16,000 statistical series by keyword.
   2. 2boc_get_observationsBanque du CanadaGet observations for one or more Bank of Canada Valet series.
   3. 3boc_search_groupsBanque du CanadaSearch Bank of Canada Valet's series groups by keyword.
   4. 4boc_get_groupBanque du CanadaGet a Bank of Canada Valet group's description and member series.
   5. 5boc_get_group_observationsBanque du CanadaGet observations for every series in a Bank of Canada Valet group.

   Les 5 premiers des 214 outils indexés, classés par le même index BM25 que search_tools.

3. 03

   Récupérercall_tool

   L'agent appelle l'outil par son nom. Le résultat est un objet typé, et son bloc de provenance, mis en évidence ici, donne l'URL exacte de la source et l'heure de la requête.

   Requête

   {"name": "boc_get_observations", "arguments": {"series_names": ["V39079"], "recent": 3}}

   Résultat

   Target for the overnight rate (business daily) V39079

   Date	Valeur
   24 septembre 2026	2,25
   23 septembre 2026	2,25
   22 septembre 2026	2,25

   Provenance

   URL de la source

   https://www.bankofcanada.ca/valet/observations/V39079/json

   Consultée le

   27 septembre 2026, 17 h 00 UTC

   Type de résultat

   boc.ObservationsResult

   Afficher la réponse complète en JSON

   {
     "series": {
       "V39079": {"name": "V39079", "label": "Target for the overnight rate (business daily)", "description": "Also called the policy interest rate, the average rate that the Bank of Canada wants to see in the market for overnight money market financing. (V39079)"}
     },
     "observations": [
       {"ref_date": "2026-09-24", "values": {"V39079": 2.25}},
       {"ref_date": "2026-09-23", "values": {"V39079": 2.25}},
       {"ref_date": "2026-09-22", "values": {"V39079": 2.25}}
     ],
     "provenance": {
       "source": "boc",
       "url": "https://www.bankofcanada.ca/valet/observations/V39079/json",
       "queried_at": "2026-09-27T17:00:48.227537Z",
       "as_of": null,
       "freshness": null,
       "coverage": null,
       "limits": null,
       "cached": false,
       "schema_name": "boc.ObservationsResult",
       "reproduce": "For R, Python, Stata and Julia scripts that fetch and clean this data, call reproduce_code with this tool's name and arguments."
     }}

4. 04

   Reproduirereproduce_code

   Le même appel devient un script R, Python, Stata ou Julia qui télécharge, vérifie et nettoie les données, pour que l'analyse ne dépende pas de l'agent.

   Requête

   {"tool_name": "boc_get_observations", "arguments": {"series_names": ["V39079"], "recent": 3}}

   RPythonStataJulia

   R

   # ============================================================
   # Bank of Canada Valet: V39079
   # Purpose: Fetch the data behind MapleStats MCP's boc_get_observations
   #          (exact: Valet request rebuilt from the tool's arguments)
   # Inputs:  https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3
   # Outputs: data/raw/valet_observations.json; the prepared table as `data`
   # ============================================================
   
   # 0. Setup ----
   
   library(dplyr)
   library(httr2)
   library(janitor)
   library(jsonlite)
   library(lubridate)
   library(readr)
   library(stringr)
   library(tibble)
   library(tidyr)
   
   dir.create("data/raw", recursive = TRUE, showWarnings = FALSE)
   
   # 1. Read inputs ----
   
   response <- request("https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3") |>
     req_perform()
   
   writeLines(resp_body_string(response), "data/raw/valet_observations.json")
   
   payload <- fromJSON("data/raw/valet_observations.json", flatten = TRUE)
   data <- as_tibble(payload[["observations"]])
   
   # 2. Check inputs ----
   
   stopifnot("https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3 returned no rows" = nrow(data) > 0)
   
   # 3. Prepare data ----
   
   data <- data |>
     clean_names()
   
   # Valet nests each series as <series>.v; make one row per date and series.
   
   data <- data |>
     pivot_longer(-d, names_to = "series", values_to = "value") |>
     mutate(
       series = str_remove(series, "_v$") |> str_to_upper(),
       value = as.numeric(value),
       date = ymd(d)
     ) |>
     select(date, series, value)
   
   # Standard cleaning: trimmed text, empty strings as missing, and numbers
   # stored as text converted to numbers.
   
   data <- data |>
     mutate(across(where(is.character), \(x) na_if(str_trim(x), ""))) |>
     type_convert()

   Python

   # ============================================================
   # Bank of Canada Valet: V39079
   # Purpose: Fetch the data behind MapleStats MCP's boc_get_observations
   #          (exact: Valet request rebuilt from the tool's arguments)
   # Inputs:  https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3
   # Outputs: data/raw/valet_observations.json; the prepared table as `data`
   # ============================================================
   
   # %% 0. Setup
   
   import json
   import re
   import unicodedata
   from pathlib import Path
   
   import httpx
   import polars as pl
   
   RAW_DIR = Path("data/raw")
   RAW_DIR.mkdir(parents=True, exist_ok=True)
   raw_path = RAW_DIR / "valet_observations.json"
   
   # %% 1. Read inputs
   
   with httpx.Client(
       http2=True,
       follow_redirects=True,
       timeout=300,
       headers={"User-Agent": "Mozilla/5.0 (compatible; research script)"},
   ) as client:
       response = client.get('https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3')
   response.raise_for_status()
   raw_path.write_bytes(response.content)
   
   payload = json.loads(raw_path.read_text(encoding="utf-8"))
   records = payload['observations']
   data = pl.json_normalize(records, strict=False)
   
   # %% 2. Check inputs
   
   assert data.height > 0, "https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3 returned no rows"
   
   # %% 3. Prepare data
   
   # Valet nests each series as <series>.v; make one row per date and series.
   
   data = data.unpivot(index="d", variable_name="series", value_name="value")
   data = data.with_columns(
       pl.col("d").str.to_date().alias("date"),
       pl.col("series").str.replace(r"\.v$", ""),
       pl.col("value").cast(pl.Float64, strict=False),
   ).select("date", "series", "value")
   
   # Standard cleaning: snake_case names without accents (PÉRIODE -> periode,
   # referenceNumber -> reference_number, as janitor does in R), trimmed text,
   # empty strings as missing. Names that clean alike are numbered as janitor
   # numbers them (Indicator, indicator -> indicator, indicator_2).
   
   clean_names = [
       re.sub(
           r"[^0-9a-z]+",
           "_",
           re.sub(
               r"([a-z0-9])([A-Z])",
               r"\1_\2",
               unicodedata.normalize("NFKD", column).encode("ascii", "ignore").decode(),
           ).lower(),
       ).strip("_")
       for column in data.columns
   ]
   while len(set(clean_names)) < len(clean_names):
       name_counts = {}
       numbered = []
       for name in clean_names:
           name_counts[name] = name_counts.get(name, 0) + 1
           count = name_counts[name]
           numbered.append(name if count == 1 else f"{name}_{count}")
       clean_names = numbered
   data = data.rename(dict(zip(data.columns, clean_names)))
   data = data.with_columns(pl.col(pl.Utf8).str.strip_chars().replace("", None))

   Stata

   * ============================================================
   * Bank of Canada Valet: V39079
   * Purpose: Fetch the data behind MapleStats MCP's boc_get_observations
   *          (exact: Valet request rebuilt from the tool's arguments)
   * Inputs:  https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3
   * Outputs: data/raw/valet_observations.json; the prepared table as `data`
   * ============================================================
   
   version 18
   clear all
   set more off
   
   * 0. Setup
   
   capture mkdir "logs"
   capture log close
   log using "logs/boc_get_observations.log", replace
   capture mkdir "data"
   capture mkdir "data/raw"
   
   * 1. Read inputs
   
   * Stata reads no JSON or HTML and truncates long column names, so its
   * built-in Python (Stata 16+) fetches, filters and writes a CSV. Point
   * Stata at a Python with these packages first: python set exec <path>.
   
   python:
   import json
   from pathlib import Path
   import httpx
   import polars as pl
   RAW_DIR = Path("data/raw")
   RAW_DIR.mkdir(parents=True, exist_ok=True)
   raw_path = RAW_DIR / "valet_observations.json"
   with httpx.Client(http2=True, follow_redirects=True, timeout=300, headers={"User-Agent": "Mozilla/5.0 (compatible; research script)"}) as client: response = client.get('https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3')
   response.raise_for_status()
   raw_path.write_bytes(response.content)
   payload = json.loads(raw_path.read_text(encoding="utf-8"))
   records = payload['observations']
   data = pl.json_normalize(records, strict=False)
   nested = [name for name, dtype in data.schema.items() if dtype.is_nested()]
   data = data.with_columns(pl.col(name).map_elements(lambda value: json.dumps(value.to_list() if isinstance(value, pl.Series) else value, default=str), return_dtype=pl.Utf8) for name in nested)
   data.write_csv(RAW_DIR / "valet_observations_prepared.csv")
   end
   
   import delimited "data/raw/valet_observations_prepared.csv", clear varnames(1) encoding("utf-8")
   
   * 2. Check inputs
   
   assert _N > 0
   
   * 3. Prepare data
   
   * One column per series (<series>_v), one row per date.
   
   generate date = date(d, "YMD")
   format date %td
   drop d
   
   * Standard cleaning: lower-case names, trimmed text, and numbers stored as
   * text converted (destring leaves genuinely non-numeric text alone).
   * rename *, lower stops at a clash (Indicator next to indicator), so names
   * that lower-case alike are numbered as janitor numbers them (indicator,
   * indicator_2), then renamed in one group rename, which allows swaps.
   
   local names
   foreach var of varlist _all {
       local names `names' `=strlower("`var'")'
   }
   local dups : list dups names
   while "`dups'" != "" {
       local numbered
       local before
       foreach name of local names {
           local count 1
           foreach earlier of local before {
               if "`earlier'" == "`name'" local ++count
           }
           local before `before' `name'
           if `count' > 1 {
               local name = substr("`name'", 1, 32 - strlen("_`count'")) + "_`count'"
           }
           local numbered `numbered' `name'
       }
       local names `numbered'
       local dups : list dups names
   }
   local old_names
   local new_names
   local i 0
   foreach var of varlist _all {
       local ++i
       local name : word `i' of `names'
       if "`name'" != "`var'" {
           local old_names `old_names' `var'
           local new_names `new_names' `name'
       }
   }
   if "`old_names'" != "" {
       rename (`old_names') (`new_names')
   }
   quietly ds, has(type string)
   local text_vars `r(varlist)'
   foreach var of local text_vars {
       replace `var' = strtrim(`var')
   }
   destring, replace
   
   log close

   Julia

   # ============================================================
   # Bank of Canada Valet: V39079
   # Purpose: Fetch the data behind MapleStats MCP's boc_get_observations
   #          (exact: Valet request rebuilt from the tool's arguments)
   # Inputs:  https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3
   # Outputs: data/raw/valet_observations.json; the prepared table as `data`
   # ============================================================
   
   # 0. Setup
   
   using DataFrames
   using Downloads
   using JSON3
   using Tables
   using TidierData
   
   mkpath("data/raw")
   
   # 1. Read inputs
   
   Downloads.download("https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3", "data/raw/valet_observations.json")
   payload = JSON3.read(read("data/raw/valet_observations.json", String))
   records = payload["observations"]
   data = DataFrame(Tables.dictrowtable(records))
   
   # 2. Check inputs
   
   @assert nrow(data) > 0 "https://www.bankofcanada.ca/valet/observations/V39079/json?recent=3 returned no rows"
   
   # 3. Prepare data
   
   # Standard cleaning: snake_case names, trimmed text, empty strings as missing.
   
   data = @chain data begin
       @clean_names
   end
   data = mapcols(
       col -> eltype(col) <: Union{Missing, AbstractString} ?
           [ismissing(x) || isempty(strip(x)) ? missing : strip(x) for x in col] : col,
       data,
   )