Lindas MCP server

Part of the Swiss Public Data MCP Portfolio — a collection of open-source MCP servers connecting AI agents to Swiss public and open data.

This is a private project. It is not affiliated with, endorsed by, or operated on behalf of any employer or public authority.

lindas-mcp

MCP server for LINDAS — the linked-data knowledge graph of the Swiss administration.

🇩🇪 Deutsche Version

What LINDAS is

LINDAS (Linked Data Service) is the Swiss Confederation's SPARQL knowledge graph, run by the Federal Archives. Instead of tables, it publishes data as RDF triples: around 2000 statistical data cubes (cube.link) from federal offices, plus the geo-linked data that powers visualize.admin.ch.

Mnemonic: «I14Y is the library catalogue, LINDAS is the library itself.»

i14y-mcp tells you a dataset exists.

LINDAS holds the data and lets you query across all of it at once.

This server wraps LINDAS in guarded tools rather than exposing raw SPARQL, because the store rewards precise queries and times out on broad ones.

🎯 Anchor Demo Query

*«Which forest-fire danger level currently applies, who publishes it, and

under which licence?»*

search_cubes(query="waldbrand")
  → «Waldbrandgefahr» — BAFU, published

get_cube_structure(cube_uri=...)
  → dimensions: Warnregion (key), Gefahrenstufe (measure)
  → licence: fedlex.data.admin.ch/eli/cc/1984/... (a Fedlex URI!)

query_cube_observations(cube_uri=...)
  → Warnregion: "Dorneck / Thierstein (SO)", Gefahrenstufe: "grosse Gefahr"

The codes come back as labels — «grosse Gefahr», not 4. And the licence is a Fedlex URI you can resolve with fedlex-mcp.

Demo

The two-phase access pattern

LINDAS cubes are self-describing but coded, and the server does two steps to read them:

its measures (the numbers), and which dimensions carry code lists.

1. Structure — the cube's SHACL shape: its dimensions (filterable axes),

using that structure.

1. Data — the observations, with coded values resolved to human labels

Mnemonic: «LINDAS speaks in postcodes, not place names.» An observation

says region 1805; the server turns that into «Alpennordhang» for you.

Both steps happen inside querycubeobservations. It fetches the structure itself, so a caller does not have to call getcubestructure first to get readable rows — doing so only repeats the cube-metadata and dimension queries. getcubestructure is the tool for finding out what a cube contains (dimension names, key vs. measure, licence) and for writing a run_sparql query against it. It reports only whether a dimension has a code list, not the entries, so it is not a decoding aid either.

Architecture

Shortened. The full README is on GitHub.