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. Reading them well means two steps, which this server enforces:

1. Structure first — get_cube_structure reads the cube's SHACL shape: its dimensions (filterable axes), its measures (the numbers), and which dimensions carry code lists.
2. Data second — query_cube_observations reads the observations and resolves coded values to human labels using the structure from step 1.

Mnemonic: «LINDAS speaks in postcodes, not place names.» An observation says region 1805; the server turns that into «Alpennordhang» for you.

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.

Installation

uvx lindas-mcp

Claude Desktop

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

Remote deployment

LINDAS_MCP_TRANSPORT=sse PORT=8000 lindas-mcp

LINDAS_MCP_TRANSPORT accepts stdio (default), sse or streamable-http. The SSE / streamable-http transport binds to HOST, default 127.0.0.1; set HOST=0.0.0.0 explicitly to expose it (only behind a reverse proxy). For a hosted HTTP deployment, set ALLOWED_ORIGINS to a comma-separated list of browser origins — unset means no browser client is permitted at all, which is the default. * is still accepted and logs a warning. LOG_LEVEL tunes the JSON stderr logs.

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.

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.

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.

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

MCP protocol version

The negotiated MCP protocol version is managed by the pinned mcp SDK (mcp>=1.28.1 in pyproject.toml), which Dependabot keeps current. SDK upgrades are therefore a reviewed change: any protocol-affecting bump is called out in CHANGELOG.md, and the tool contract is guarded independently by tool-definitions.lock.json (SEC-022) so a change to the tool surface fails CI until reviewed.