{
  "markdown": "# mcp-istat-it\n\nISTAT (Istituto Nazionale di Statistica) MCP — Italy's national statistics\n\nPart of [Pipeworx](https://pipeworx.io) — an MCP gateway connecting AI agents to 1476+ live data sources.\n\n## Tools\n\n| Tool | Description |\n|------|-------------|\n| `list_dataflows` | Browse or keyword-search ISTAT datasets (dataflows). Each result has an `id` (the dataflowId you pass to get_data / dataflow_structure) and an English name. ISTAT publishes ~4,800 datasets, so always pass `query` to filter unless you really want the whole list. Example: list_dataflows({ query: \"unemployment\" }) or list_dataflows({ query: \"GDP\" }). |\n| `dataflow_structure` | Get the structure (Data Structure Definition) of one ISTAT dataset: its ordered dimensions and, for each, the valid codes. Use this to learn how to build the dot-separated SDMX key for get_data. The key positions correspond to the dimensions in order; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: \"101_1015\" }). |\n| `get_data` | Pull observations from an ISTAT 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. Example: get_data({ dataflow_id: \"101_1015\", key: \"A.IT...\", start_period: \"2021\", end_period: \"2023\" }) selects annual (A), REF_AREA=IT (Italy), and wildcards the rest. Omit `key` (or pass \"\") to fetch all series — caution, this can be large. Returns decoded series with their dimension labels and per-period values. |\n\n## Quick Start\n\nAdd to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):\n\n```json\n{\n  \"mcpServers\": {\n    \"istat-it\": {\n      \"url\": \"https://gateway.pipeworx.io/istat-it/mcp\"\n    }\n  }\n}\n```\n\n### What this endpoint actually serves\n\n`tools/list` at `https://gateway.pipeworx.io/istat-it/mcp` returns the tools in the table\nabove **plus the shared Pipeworx meta-tools** — `ask_pipeworx`,\n`discover_tools`, `search_within`, `remember`/`recall` and the rest of the\ngateway-wide set. So the tool count you see is larger than this table: a\nsingle-pack endpoint currently lists roughly 30 shared tools alongside the\npack's own. The connection's `initialize` response states its exact scope, and\nis the authoritative answer for a given day.\n\nThis is deliberate, not multiplexing by accident. The meta-tools are what let a\nscoped connection answer a question this pack does not cover — via\n`ask_pipeworx`, which routes across the whole catalog — without you adding a\nsecond MCP server. There is currently no way to mount a pack endpoint without\nthem; if the extra schemas cost you more context than the routing is worth,\nconnect to the full gateway once rather than to several pack endpoints.\n\nOr connect to the full Pipeworx gateway to get every pack's tools listed\ndirectly, instead of just this one's:\n\n```json\n{\n  \"mcpServers\": {\n    \"pipeworx\": {\n      \"url\": \"https://gateway.pipeworx.io/mcp\"\n    }\n  }\n}\n```\n\nBoth URLs reach the same gateway and the same 1476+ data sources. The\nonly difference is which pack's tools are listed **directly**; `ask_pipeworx`\nreaches all of them from either one.\n\n## Using with ask_pipeworx\n\nInstead of calling tools directly, you can ask questions in plain English —\nthis works on the pack endpoint above as well as on the full gateway:\n\n```\nask_pipeworx({ question: \"your question about Istat It data\" })\n```\n\nThe gateway picks the right tool and fills the arguments automatically.\n\n## More\n\n- [Docs and guides](https://pipeworx.io/docs)\n- [pipeworx.io](https://pipeworx.io)\n\n## License\n\nMIT\n",
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