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Swiss federal law, consultations (Fedlex) & TERMDAT terminology (LINDAS) via SPARQL
io.github.malkreide/fedlex-mcp is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding io.github.malkreide/fedlex-mcp to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
uvx fedlex-mcp==2.1.0Review the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-09-27T16:41:48.830308Z",
"publishedAt": "2026-09-27T16:41:48.830308Z",
"statusChangedAt": "2026-09-27T16:41:48.830308Z"
}
},
"server": {
"name": "io.github.malkreide/fedlex-mcp",
"_meta": {
"io.modelcontextprotocol.registry/publisher-provided": {
"scope": "core",
"category": "Legal, Courts & Regulatory"
}
},
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "2.1.0",
"packages": [
{
"version": "2.1.0",
"transport": {
"type": "stdio"
},
"identifier": "fedlex-mcp",
"runtimeHint": "uvx",
"registryType": "pypi"
}
],
"repository": {
"url": "https://github.com/malkreide/fedlex-mcp",
"source": "github"
},
"websiteUrl": "https://github.com/malkreide/fedlex-mcp",
"description": "Swiss federal law, consultations (Fedlex) & TERMDAT terminology (LINDAS) via SPARQL"
}
}