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eurostat-mcp-server

https://eurostat.caseyjhand.com

Registry code: bcc876467b23f25c

api record

Eurostat MCP server — EU statistical data across the Eurostat catalogue.

Workflow: eurostat_search_datasets or eurostat_browse_themes to find a dataset code → eurostat_get_dataset_info to see dimensions → eurostat_get_dimension_values to list valid filter values → eurostat_query_dataset to fetch observations.

endpoint
https://eurostat.caseyjhand.com/mcp
protocol
http-sse ·2025-06-18
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_ used through this hub 30 days

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_ what it can do 8 tools
8 never probed 0 of 8 classified

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  • eurostat_search_datasets unknown never probed

    Search the Eurostat catalogue by keyword. Returns matching datasets with codes, descriptions, period coverage, and theme breadcrumbs. Use this to discover dataset codes before calling eurostat_get_dataset_info, then eurostat_query_dataset for a slice of a dataset or eurostat_download_dataset for the whole of one. Results are limited to datasets and predefined tables — folders are excluded.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "default": 20,
      "maximum": 100,
      "minimum": 1,
      "description": "Page size — maximum datasets returned per page (1–100). Default is 20. To retrieve matches beyond one page, pass the returned nextCursor back as cursor; the page size is fixed by this first call."
    },
    "query": {
      "type": "string",
      "pattern": "\\S",
      "minLength": 1,
      "description": "Search terms — at least one non-whitespace token is required. Split on whitespace into tokens; every token must match (AND), case-insensitively, somewhere across the dataset label, theme breadcrumb, or code. Word order does not matter, so \"business demography NUTS 3\" or \"regional economic accounts\" resolve without naming a label verbatim."
    },
    "cursor": {
      "type": "string",
      "description": "Opaque pagination cursor from a previous call's nextCursor. Omit for the first page; pass it back — with the same query — to fetch the next page of matches over a stable order. A cursor is bound to the query that produced it and to the catalogue snapshot in effect at that time, so reusing one with a different query, or after the catalogue refreshes, is rejected rather than silently paging a different result set."
    }
  },
  "additionalProperties": false
}
    arguments 27 lines
  • eurostat_browse_themes unknown never probed

    Navigate the Eurostat theme tree. Without theme_code returns the top-level theme folders (Economy, Population, Transport, etc.) — the practical starting points. With a theme_code returns its immediate children: subtheme folders and datasets in that branch. Use this for structured discovery when you know the domain but not the dataset code, or to drill down from a broad topic to a specific dataset. Pair with eurostat_search_datasets for keyword-based discovery.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "theme_code": {
      "type": "string",
      "description": "Folder code to expand (e.g., \"economy\", \"reg\"). Omit to list the top-level theme folders."
    }
  },
  "additionalProperties": false
}
    arguments 11 lines
  • eurostat_get_dataset_info unknown never probed

    Fetch metadata for a Eurostat dataset: dimensions with valid values, time range, observation count, and last-update date. Call this before eurostat_query_dataset or eurostat_download_dataset to discover what dimension codes are valid (unit, na_item, geo, etc.); eurostat_download_dataset builds its positional filter key from this dimension list, so a filter naming a dimension absent here is rejected outright. Returns up to 10 sample values per dimension for orientation; use eurostat_get_dimension_values to list the full set for large dimensions.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "dataset_code"
  ],
  "properties": {
    "dataset_code": {
      "type": "string",
      "minLength": 1,
      "description": "Dataset code (e.g., \"nama_10_gdp\"). Use eurostat_search_datasets or eurostat_browse_themes to find codes."
    }
  },
  "additionalProperties": false
}
    arguments 15 lines
  • eurostat_get_dimension_values unknown never probed

    List all valid values for a specific dimension in a Eurostat dataset (e.g., all unit codes for nama_10_gdp, all geo codes for a regional dataset). Use this when eurostat_get_dataset_info returns more values than the 10-item sample, or to confirm exact codes before querying. For the "geo" dimension, use geo_level to filter by NUTS hierarchy (country, nuts1, nuts2, nuts3). Invalid dimension_value codes silently return no data from eurostat_query_dataset, and are rejected by Eurostat as a fault on eurostat_download_dataset; use this tool to verify codes first.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "dataset_code",
    "dimension"
  ],
  "properties": {
    "dimension": {
      "type": "string",
      "minLength": 1,
      "description": "Dimension code to retrieve values for (e.g., \"unit\", \"na_item\", \"geo\"). Use eurostat_get_dataset_info to see available dimensions."
    },
    "geo_level": {
      "enum": [
        "aggregate",
        "country",
        "nuts1",
        "nuts2",
        "nuts3"
      ],
      "type": "string",
      "description": "NUTS hierarchy level filter — applies only when dimension is \"geo\"; passing it with any other dimension is rejected. Options: \"aggregate\" (EU/EA codes), \"country\" (2-letter codes, default), \"nuts1\" (3-char), \"nuts2\" (4-char), \"nuts3\" (5-char)."
    },
    "dataset_code": {
      "type": "string",
      "minLength": 1,
      "description": "Dataset code (e.g., \"nama_10_gdp\")."
    }
  },
  "additionalProperties": false
}
    arguments 32 lines
  • eurostat_query_dataset unknown never probed

    Fetch statistical data from a Eurostat dataset with dimension filters. Returns a deterministic inline prefix of decoded observations with dimension codes and labels, numeric values, an OBS_FLAG status (e.g., "p" = provisional, "e" = estimated) and a separate CONF_STATUS confidentiality marker (e.g., "C" = confidential, which is usually why a value is null). preview_limit controls only that prefix; filters and period controls reduce the matched result itself. Call eurostat_get_dataset_info first to discover valid dimension codes and values. Apply filters to keep the result set manageable — large unfiltered queries may trigger an async response error. Use filters.geo for specific country/region codes, or geo_level for NUTS hierarchy filtering (mutually exclusive). Use last_n_periods for the N most recent periods without knowing the end date. Matches above 5,000 observations are staged whole when this deployment runs a dataframe canvas: call eurostat_dataframe_describe first, then eurostat_dataframe_query. Matches at or below 5,000 are never staged. When the target is a whole dataset rather than a slice, eurostat_download_dataset reads the SDMX bulk endpoint instead and is the cheaper route.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "dataset_code"
  ],
  "properties": {
    "lang": {
      "enum": [
        "EN",
        "FR",
        "DE"
      ],
      "type": "string",
      "default": "EN",
      "description": "Language for labels in the response. Default is \"EN\". Options: \"EN\", \"FR\", \"DE\"."
    },
    "filters": {
      "type": "object",
      "default": {},
      "description": "Dimension filters as a map of dimension code → array of valid values. Example: {\"unit\": [\"CP_MEUR\"], \"na_item\": [\"B1GQ\"], \"geo\": [\"DE\", \"FR\"]}. An empty array is treated as no filter for that dimension and is dropped from the request. Do not include \"geo\" here if using geo_level. Invalid dimension values silently return no data — verify with eurostat_get_dimension_values first.",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    },
    "canvas_id": {
      "type": "string",
      "pattern": "^[A-Za-z0-9_-]{10}$",
      "description": "Reuse an existing dataframe canvas, so a result staged by this call lands beside earlier ones and can be joined against them. Pass the canvasId a previous eurostat_query_dataset or eurostat_download_dataset response returned; omit to start a fresh canvas. Ignored on deployments without a dataframe canvas and when the match is at or below 5,000 observations."
    },
    "geo_level": {
      "enum": [
        "aggregate",
        "country",
        "nuts1",
        "nuts2",
        "nuts3"
      ],
      "type": "string",
      "description": "Filter by NUTS hierarchy level. Mutually exclusive with a \"geo\" key in filters. Options: \"aggregate\" (EU/EA totals), \"country\" (41 member/candidate states), \"nuts1\" (127 major regions), \"nuts2\" (309 basic regions), \"nuts3\" (1,343 small regions)."
    },
    "dataset_code": {
      "type": "string",
      "minLength": 1,
      "description": "Dataset code (e.g., \"nama_10_gdp\"). Required."
    },
    "since_period": {
      "type": "string",
      "description": "Start of time range (e.g., \"2020\", \"2023-Q1\", \"2024-01\"). Mutually exclusive with last_n_periods."
    },
    "until_period": {
      "type": "string",
      "description": "End of time range (e.g., \"2024\"). Omit for data through the latest available period. Mutually exclusive with last_n_periods."
    },
    "preview_limit": {
      "type": "integer",
      "default": 50,
      "maximum": 500,
      "minimum": 1,
      "description": "How many matched observations to return inline, from the deterministic start of the JSON-stat cell order. Default 50; maximum 500. This changes only the inline prefix: it does not reduce obsCount, missingObsCount, timeRange, the upstream response, or the rows staged when the match exceeds 5,000. Use filters or period controls to reduce the match itself."
    },
    "last_n_periods": {
      "type": "integer",
      "maximum": 9007199254740991,
      "minimum": 1,
      "description": "Return only the N most recent periods. Mutually exclusive with since_period and until_period."
    }
  },
  "additionalProperties": false
}
    arguments 76 lines
  • eurostat_download_dataset unknown never probed

    Download a Eurostat dataset in bulk through the SDMX 2.1 TSV endpoint and stage every observation as a SQL table on the dataframe canvas — the route to a whole dataset, where eurostat_query_dataset is the route to a slice of one. The TSV wire format is roughly half the bytes of the JSON-stat body eurostat_query_dataset reads, so it reaches datasets that would otherwise time out, and it is expanded here into one row per observation. Filters take the same dimension-code map eurostat_query_dataset uses and are applied server-side by Eurostat; call eurostat_get_dataset_info first for the dimension codes and eurostat_get_dimension_values for their values. Narrow with since_period/until_period rather than asking for the most recent N periods — the TSV layout keeps a column for every period whichever is requested, so a period range is what actually shrinks the response. Transfers are bounded by a byte budget enforced while streaming: when it is spent the download stops and budgetExceeded is set, leaving a prefix of the dataset rather than an error. Only preview_limit rows come back inline. When a table is staged, call eurostat_dataframe_describe first to confirm its columns, then eurostat_dataframe_query; without a canvas, rows past the preview are not retained.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "dataset_code"
  ],
  "properties": {
    "filters": {
      "type": "object",
      "default": {},
      "description": "Dimension filters as a map of dimension code → array of accepted values, applied by Eurostat before the body is sent. Example: {\"unit\": [\"CP_MEUR\"], \"na_item\": [\"B1G\"], \"geo\": [\"DE\", \"FR\"]}. Omit a dimension or pass an empty array to accept every value for it. Do not put \"time\" here — use since_period/until_period. Naming a dimension the dataset does not have is rejected with the dataset's dimension list rather than silently ignored.",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    },
    "canvas_id": {
      "type": "string",
      "pattern": "^[A-Za-z0-9_-]{10}$",
      "description": "Reuse an existing dataframe canvas so this download lands beside earlier results and can be joined against them. Pass the canvasId a previous eurostat_download_dataset or eurostat_query_dataset response returned; omit to start a fresh canvas. Ignored on deployments without a dataframe canvas."
    },
    "dataset_code": {
      "type": "string",
      "minLength": 1,
      "description": "Dataset code (e.g., \"nama_10_gdp\"). Required."
    },
    "since_period": {
      "type": "string",
      "description": "Start of the period range (e.g., \"2020\", \"2023-Q1\", \"2024-01\"), sent as startPeriod. The most effective way to shrink a bulk response: it removes period columns from the TSV rather than blanking their cells."
    },
    "until_period": {
      "type": "string",
      "description": "End of the period range (e.g., \"2024\"), sent as endPeriod. Omit for data through the latest available period."
    },
    "preview_limit": {
      "type": "integer",
      "default": 50,
      "maximum": 500,
      "minimum": 1,
      "description": "How many observations to echo inline, from the start of the download. Caps at 500. The full download is on the canvas table when one was staged; this is orientation, not the result set."
    }
  },
  "additionalProperties": false
}
    arguments 49 lines
  • eurostat_dataframe_describe unknown never probed

    List the tables staged on a Eurostat dataframe canvas, with their row counts and column names and types. Call this before eurostat_dataframe_query to learn the table and column names to write SQL against. The canvas_id comes from a eurostat_query_dataset or eurostat_download_dataset response that reported a staged table. Every observation column is flat, but the two stagers write different dimension columns, so read the columns reported here rather than assuming: eurostat_query_dataset gives each dimension a code column named after the dimension (e.g. "geo") plus a label companion (e.g. "geo_label"); eurostat_download_dataset gives code columns only — the bulk endpoint carries no labels — plus a "time" column. Both write the same five measure columns — obs_value, obs_flag, obs_flag_label, conf_status, conf_status_label — carrying the same codes for the same observation, so tables from the two stagers join on dimension codes and time and compare like with like.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "canvas_id"
  ],
  "properties": {
    "canvas_id": {
      "type": "string",
      "pattern": "^[A-Za-z0-9_-]{10}$",
      "description": "Canvas identifier returned as canvasId by eurostat_query_dataset or eurostat_download_dataset. Identifies the workspace holding the staged tables."
    }
  },
  "additionalProperties": false
}
    arguments 15 lines
  • eurostat_dataframe_query unknown never probed

    Run a read-only SQL SELECT against tables staged on a Eurostat dataframe canvas — the way to reach observations past the 5,000-row inline cap of eurostat_query_dataset and past the inline preview of a eurostat_download_dataset bulk download, and to aggregate, group, or join across staged tables without re-fetching from Eurostat. Call eurostat_dataframe_describe first for the table and column names, which differ between the two stagers. Only a single SELECT statement runs: statement chaining, non-SELECT verbs, and functions that read files or external data are rejected. Columns are flat — every dimension is a code column named after the dimension, the measure is obs_value, the observation flag is obs_flag / obs_flag_label and the confidentiality marker is conf_status / conf_status_label; a "_label" companion per dimension exists only on tables eurostat_query_dataset staged. Both stagers write the same five measure columns with the same codes, so join their tables on dimension codes and time and compare obs_flag or conf_status across them directly.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "canvas_id",
    "sql"
  ],
  "properties": {
    "sql": {
      "type": "string",
      "minLength": 1,
      "description": "A single read-only SELECT statement. Reference tables by the names eurostat_dataframe_describe reports. Example: SELECT geo, geo_label, AVG(obs_value) AS mean FROM df_a1b2c3d4 WHERE time >= '2020' GROUP BY geo, geo_label ORDER BY mean DESC."
    },
    "canvas_id": {
      "type": "string",
      "pattern": "^[A-Za-z0-9_-]{10}$",
      "description": "Canvas identifier returned as canvasId by eurostat_query_dataset or eurostat_download_dataset. Identifies the workspace holding the staged tables."
    }
  },
  "additionalProperties": false
}
    arguments 21 lines
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