Lindas

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:

1. Structure — the cube's SHACL shape: its dimensions (filterable axes), its measures (the numbers), and which dimensions carry code lists.
2. Data — the observations, with coded values resolved to human labels using that structure.

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 query_cube_observations. It fetches the structure itself, so a caller does not have to call get_cube_structure first to get readable rows — doing so only repeats the cube-metadata and dimension queries. get_cube_structure 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

                 ┌──────────────────────────────┐
                 │      MCP Host (Claude)       │
                 └───────────────┬──────────────┘
                                 │ stdio | streamable-http
                 ┌───────────────▼──────────────┐
                 │          lindas-mcp          │
                 │  ┌────────────────────────┐  │
                 │  │ server.py  (7 tools)   │  │  talks only to cube.py
                 │  ├────────────────────────┤  │
                 │  │ lindas/cube.py         │  │  ← vocabulary guardrail,
                 │  │                        │  │    two-phase access,
                 │  │                        │  │    code→label resolution
                 │  ├────────────────────────┤  │
                 │  │ lindas/queries.py      │  │  SPARQL templates,
                 │  │                        │  │    all anchored on a class
                 │  ├────────────────────────┤  │
                 │  │ lindas/client.py       │  │  raw SPARQL over HTTP,
                 │  │                        │  │    knows nothing of cubes
                 │  └────────────────────────┘  │
                 └───────────────┬──────────────┘
                                 │ HTTPS, no auth
                 ┌───────────────▼──────────────┐
                 │  lindas.admin.ch/query       │
                 │  SPARQL 1.1 · ~2000 cubes    │
                 └──────────────────────────────┘

The lindas/ package is deliberately layered so it can be lifted into other LINDAS-backed servers unchanged. client.py knows only HTTP and SPARQL; cube.py knows the cube.link vocabulary; the tools know only cube.py. Raw SPARQL never reaches the agent except through the guarded run_sparql escape hatch.

Architecture decision

Architecture A (live SPARQL only), with a strict vocabulary guardrail.

Verified live on 2026-07-21:

• The endpoint is stable, needs no authentication, and returns a clean HTTP 400 with a diagnostic on malformed queries.
• Blind scans (SELECT *, COUNT(*) over the whole store) time out at 60–90 s; the same question anchored on ?x a cube:Cube answers in ~2 s.

Consequences, baked into the tools:

• Every query template is anchored on a known class. No unbounded scans.
• Two-phase access is enforced; the agent never sees raw codes.
• run_sparql is capped at 500 rows and 30 s and marked as advanced.
• The client timeout sits at 45 s, in front of the store's own 60–90 s abort.

Full probe report: docs/probe-lindas.md.

Tools

All tools are annotated readOnlyHint: true.

Reading a search_cubes result

returned is a count of what came back, not a statement about what exists. Two fields say how complete the answer is:

Check truncated before concluding anything. On true, do not report the result as complete and do not answer "there are N cubes about X" from returned. Widen in this order: raise limit (up to 100); if it is still truncated at 100, narrow with creator_uri from list_publishers and ask one federal body at a time; use latest_only=False only when you actually want the version history, because it widens the result rather than escaping the cut.

total_matched is null more often than you might expect, and on purpose. With latest_only=true (the default) the count LINDAS can answer cheaply counts cube versions, while the tool returns version-collapsed cubes. Measured on 2026-09-20 with German labels: «wald» matches 127 published versions that collapse to 35 logical cubes, «energie» 33 to 13. A total_matched of 127 next to a returned of 20 would claim 107 missing cubes where at most 15 exist to find — so the field says null, which is true, instead of a number that is not. truncated stays reliable there and is the field to act on.

Where the server has seen every matching row — which is the common case, because it fetches one row beyond what it needs — total_matched is exact in both branches, and truncated is derived from it rather than from the fetch limit.

Installation

uvx lindas-mcp

Claude Desktop

{
  "mcpServers": {
    "lindas": {
      "command": "uvx",
      "args": ["lindas-mcp"]
    }
  }
}

Remote deployment

LINDAS_MCP_TRANSPORT=streamable-http PORT=8000 lindas-mcp

LINDAS_MCP_TRANSPORT accepts stdio (default), streamable-http or sse. The transport decides the path: streamable-http serves /mcp, sse serves /sse. Anything else falls through to stdio, which opens no port at all — in a container that surfaces only as a failing health check. Both HTTP transports bind to HOST, default 127.0.0.1; set HOST=0.0.0.0 explicitly to expose it (only behind a reverse proxy). LOG_LEVEL tunes the JSON stderr logs.

Hosting it as a remote connector

The connector URL is https://<host>/mcp. The path is not configurable — it comes from the transport, and streamable-http is the one a current client expects. sse and its /sse path are the specification's superseded transport; nothing was removed and they still serve, but a new connector should not be pointed at them.

A hosted deployment — Railway, Fly, a container behind any reverse proxy — needs three variables, and the third is the one a deployment inherits wrongly because nothing fails loudly without it:

LINDAS_MCP_ALLOWED_HOSTS is a comma-separated list of hostnames, without scheme and without port: lindas-mcp.example.ch,alias.example.ch, not https://lindas-mcp.example.ch:443. The value is matched literally against the incoming Host header, and behind TLS on port 443 that header carries no port. Loopback forms are added automatically, so the container health check keeps working.

It fails in two opposite directions:

• Unset on a non-loopback bind: the protection is off altogether. No hostname is derivable in that situation — the server is reached under a service or public DNS name this process does not know, and a guessed list would answer every real request with 421. So no allow-list is installed at all and the Host header is never checked, which is the SDK's own default. The only sign is a startup warning, dns_rebinding_protection_off.
• Set to the wrong name: every real request gets HTTP 421. The match is exact and port-precise — mcp.example.ch does not cover Host: mcp.example.ch:8443. If the proxy forwards a non-default port, name both forms.

ALLOWED_ORIGINS is a separate question and concerns browser clients only. It is the CORS origin list, comma-separated, and unset means no browser client is permitted at all — that is the default. A client that is not a browser sends no Origin and is unaffected. * is still accepted and logs a warning. One detail worth knowing if you do serve browsers: the origins derived from LINDAS_MCP_ALLOWED_HOSTS are the http:// ones, so an https:// browser origin has to be named in ALLOWED_ORIGINS yourself.

Docker

docker compose up --build          # binds 0.0.0.0 inside the container, publishes :8000

The image runs as a non-root user, read-only, with resource limits and a TCP health check (see Dockerfile and compose.yaml).

Join keys

LINDAS is a connector layer, and two of its identifiers make it composable with the rest of the portfolio:

The Fedlex link is the quiet surprise: many cubes declare their licence as a legal-basis URI (fedlex.data.admin.ch/eli/cc/...), so you can go from a data point straight to the law that governs it.

Known limitations

Verified live on 2026-07-21.

1. Broad SPARQL times out. The store aborts unanchored scans at 60–90 s. The guarded tools avoid this; run_sparql warns about it and caps runtime.
2. Observations are coded. Dimension values are URIs, not labels. The server resolves them via each dimension's code list, but resolution costs one extra query per coded dimension. Set resolve_labels=False to skip it.
3. No server-side observation filtering by arbitrary value. LINDAS has no cheap way to filter observations by a dimension value inside a cube, so query_cube_observations reads the first N observations. Analytical slicing belongs in run_sparql.
4. Licences vary per cube and are declared as dcterms:license, frequently a Fedlex URI rather than a plain name. Always surface the licence field.
5. Version handling is heuristic. search_cubes deduplicates by stripping the version suffix from the cube URI and keeping the highest schema:version among published cubes. Unusual URI shapes may not collapse cleanly; use latest_only=False to inspect every version. The same heuristic is why total_matched can be null: it is a URI-shape guess in Python, and no cheap SPARQL count expresses it, so restating it in a query would put the same guess in a second place where it can drift.

MCP Protocol Version

This server speaks two protocol eras over the same endpoint. The client's first request on a connection decides which one applies; a later claim from the other era is refused.

Both revisions are pinned in tests/test_protocol_version.py and asserted against the installed SDK, so a Dependabot bump of mcp cannot move either one silently. The handshake ceiling is measured against a live initialize through the assembled ASGI stack, not read off a constant name.

Note that the SDK's LATEST_PROTOCOL_VERSION is an alias for the modern era, not for the handshake era — pinning against it alone would leave the era that current clients actually negotiate free to drift.

What 2026-07-28 changes here

The modern era has no initialize handshake, and therefore no handshake result in which a client learns who it is talking to. Three consequences are served explicitly rather than left at the SDK's defaults:

All of it is measured through the assembled stack in tests/test_spec_2026_07_28.py — both eras, both transports, and both branches of the log opt-in. The tool contract itself is guarded independently by tool-definitions.lock.json (SEC-022), and the modern era is asserted to serve exactly the tools listed there, so the two eras cannot drift into two different servers behind one address.

Update policy. When the gate fails, do not edit the constant blindly: read the spec changelog between the two revisions, verify the server still behaves, then move the constant, this section, README.de.md and CHANGELOG.md together. SDK upgrades are a reviewed change for the same reason: any protocol-affecting bump is called out in CHANGELOG.md.

Testing

PYTHONPATH=src pytest tests/ -m "not live"   # offline, used in CI
PYTHONPATH=src pytest tests/ -m "live"       # hits the real endpoint
python -m ruff check src tests

The live tests earn their place: the observationSet indirection (a cube's observations hang off cube:observationSet, never directly off the cube) is a structural assumption that a mock cannot validate. It is covered by a live test.

Contributing

See CONTRIBUTING.md for the ground rules (read-only, one egress host, anchored queries) and the local dev loop. Further reading: EXAMPLES.md for use cases by audience with the tool-selection table, docs/roadmap.md for the project phase, and PUBLISHING.md for the PyPI / MCP Registry release process.

Security

See SECURITY.md for the security posture and how to report a vulnerability.

License

MIT License — see LICENSE. The LINDAS data remains subject to the licence each publisher declares on the cube.

Author

Hayal Oezkan · github.com/malkreide

Credits & related projects

• Data: LINDAS Linked Data Service, Swiss Federal Archives
• Vocabulary: cube.link
• Visualisation frontend on the same cubes: visualize.admin.ch
• Source discovery inspired by rnckp/awesome-ogd-switzerland
• Portfolio: swiss-public-data-mcp

Licence: MIT. The cube data remains subject to the licence each publisher declares.

MCP Registry

Ownership marker used by the MCP Registry to link this PyPI package to the GitHub namespace:

mcp-name: io.github.malkreide/lindas-mcp