Statec Lu

mcp-statec-lu

STATEC (Institut national de la statistique et des études économiques du

Part of Pipeworx — an MCP gateway connecting AI agents to 1476+ live data sources.

Tools

Tool	Description
list_dataflows	Browse or keyword-search STATEC (Luxembourg statistics) datasets, called "dataflows". Each result has an id (e.g. "DF_A1100", the dataflowRef you pass to get_data / dataflow_structure) and an English name plus a short description (publication date, periodicity, author, category). STATEC publishes hundreds of datasets, so pass query to filter unless you really want the whole catalog. Example: list_dataflows({ query: "population" }) or list_dataflows({ query: "unemployment" }).
dataflow_structure	Get the structure (Data Structure Definition) of one STATEC dataset: its ordered dimensions and, for each, the valid codes. Use this BEFORE get_data to learn how to build the dot-separated SDMX key. The key has one position per dimension, in dimension_order; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "DF_A1100" }).
get_data	Pull observations from a STATEC dataset. key is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. Fetch dataflow_structure first to know the dimension order and valid codes. Example: get_data({ dataflow_id: "DF_A1100", key: "Valeur..A", start_period: "2010", end_period: "2020" }) picks VARIABLE=Valeur, wildcards SPECIFICATION, FREQ=A (annual). Omit key (or pass "") to fetch all series — caution, this can be large. Returns decoded series with their dimension labels and per-period values.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "statec-lu": {
      "url": "https://gateway.pipeworx.io/statec-lu/mcp"
    }
  }
}

What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/statec-lu/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Both URLs reach the same gateway and the same 1476+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

ask_pipeworx({ question: "your question about Statec Lu data" })

The gateway picks the right tool and fills the arguments automatically.

More

• Docs and guides
• pipeworx.io

License

MIT