LINDAS MCP Server

A Model Context Protocol server that enables LLMs to query structured data from LINDAS — the Swiss Federal Archives' Linked Data platform at https://ld.admin.ch.

LINDAS stores multi-dimensional statistical data as RDF cubes using the cube.link vocabulary, accessible via a SPARQL endpoint backed by Stardog. This server translates high-level tool calls into SPARQL queries and returns clean, LLM-friendly JSON.

What this enables

Ask an LLM questions about Swiss federal data and let it discover, inspect, and query datasets automatically:

• "Show me forest fire danger warnings in the last week"

• "Compare population across all cantons for 2023"

• "Find datasets about unemployment"

The LLM uses the tools below to discover cubes, inspect their structure, find valid dimension values, and query observations — all without writing SPARQL.

Related MCP server: mcp-i14y

Prerequisites

• Node.js ≥ 20

• npm or pnpm

Installation

npm install
npm run build

Configuration

Environment variables (all optional):

Command-line flags override environment variables:

lindas-mcp [--transport stdio|http] [--port PORT]

Usage with Claude Desktop

Add the server to your claude_desktop_config.json:

{
  "mcpServers": {
    "lindas": {
      "command": "node",
      "args": ["C:/path/to/lindas-mcp/dist/index.js"],
      "env": {
        "LINDAS_DEFAULT_LANGUAGE": "de"
      }
    }
  }
}

For development with hot reload, use tsx:

{
  "mcpServers": {
    "lindas": {
      "command": "npx",
      "args": ["tsx", "C:/path/to/lindas-mcp/src/index.ts"]
    }
  }
}

HTTP Transport (Streamable HTTP)

For use with the MCP Inspector, web-based clients, or remote access, start the server in HTTP mode:

# Using npm scripts
npm run start:http          # node dist/index.js --transport http
npm run dev:http            # tsx src/index.ts --transport http

# Or directly
node dist/index.js --transport http --port 3000

The server exposes the MCP Streamable HTTP endpoint at http://127.0.0.1:3000/mcp.

In HTTP mode, each client session gets its own MCP server instance. The server tracks sessions via the Mcp-Session-Id header.

MCP Inspector

To inspect the server interactively, open the MCP Inspector and connect to:

http://127.0.0.1:3000/mcp

Or launch the Inspector with the server:

npx @modelcontextprotocol/inspector node dist/index.js --transport stdio

opencode

For HTTP mode in opencode, configure opencode.json:

{
  "mcp": {
    "lindas": {
      "type": "remote",
      "url": "http://127.0.0.1:3000/mcp",
      "enabled": true
    }
  }
}

LibreChat / Docker

Build the Docker image and add it to your docker-compose.yml:

services:
  lindas-mcp:
    image: lindas-mcp
    container_name: lindas-mcp
    environment:
      - LINDAS_TRANSPORT=http
      - LINDAS_PORT=8000
      - LINDAS_DEFAULT_LANGUAGE=de
    restart: unless-stopped
    # ports:                    # Only needed for host access
    #   - "8000:8000"

Then in LibreChat's config:

mcpServers:
  lindas:
    type: streamable-http
    url: http://lindas-mcp:8000/mcp

Make sure both containers share the same Docker network.

Available Tools

Discovery & Search

Querying

Geography

Resources

• lindas:///cubes — Catalogue of available data cubes (Markdown)

Prompts

• data_exploration — Step-by-step guide for exploring LINDAS data

• canton_comparison — Compare a topic across cantons for a given year (args: topic, year)

Typical Workflow

The recommended workflow for an LLM using this server:

1. search_datasets or list_cubes — Find cubes matching the user's topic

2. get_cube_structure — Inspect the cube's dimensions and measures

3. get_dimension_summary — Get a quick overview of all dimensions with value counts (replaces calling get_dimension_values for each dimension separately)

4. resolve_geography — If the user mentions a place name, resolve it to an IRI

5. resolve_iri — If query results contain opaque IRIs, look up their labels

6. count_observations — Check how many results the query will return

7. query_observations — Retrieve the data (use resolve_labels: true to get human-readable labels instead of IRIs)

For geographic comparisons, use get_cantons, get_municipalities, or get_districts to list geographic entities with their IRIs.

resolve_labels Feature

When query_observations is called with resolve_labels: true, IRI-valued dimensions are automatically joined to their schema:name labels. Instead of receiving:

{ "canton": { "value": "https://ld.admin.ch/canton/1", "label": "https://ld.admin.ch/canton/1" } }

You receive:

{ "canton": { "value": "https://ld.admin.ch/canton/1", "label": "Zürich" } }

This makes results immediately understandable without additional lookups.

Development

npm run dev          # Start stdio transport with tsx (hot reload)
npm run dev:http     # Start HTTP transport with tsx (hot reload)
npm run build        # Compile with tsc
npm start            # Run compiled stdio server
npm run start:http   # Run compiled HTTP server
npm test             # Run unit tests (vitest)
npm run test:watch   # Watch mode

Project Structure

src/
├── index.ts              # MCP server entry point (stdio + HTTP)
├── config.ts             # Configuration constants
├── sparql/
│   ├── client.ts         # SPARQL HTTP client + SparqlError
│   ├── queryBuilder.ts   # Pure SPARQL query builder functions
│   └── resultParser.ts   # SPARQL JSON → domain object parsers
├── tools/
│   ├── index.ts          # Tool registration + dispatch
│   └── *.ts              # Individual tool handlers
├── resources/
│   └── catalogue.ts      # lindas:///cubes resource
└── prompts/
    └── templates.ts      # Prompt templates
tests/
├── sparql.test.ts        # Query builder unit tests
└── resultParser.test.ts  # Parser unit tests

Notes

• All logging goes to stderr (stdout is reserved for the MCP protocol in stdio mode).

• User input interpolated into SPARQL is escaped to prevent injection.

• Result limit is capped at 500 to protect LLM context windows.

• Labels are fetched via schema:name with language filtering; IRI-valued dimensions fall back to the IRI itself if no label is found.

• The search_datasets tool uses CONTAINS filters on schema:name and schema:description (the Stardog textMatch predicate is not supported on the public LINDAS endpoint).

• In HTTP mode, each client session gets its own MCP server instance. Sessions are tracked via the Mcp-Session-Id header and cleaned up on disconnect.

License

MIT