SIP News MCP
This server provides an MCP connector for AI assistants to search, browse, and read official Luxembourg government press service (SIP) news articles in German, French, or English, with both keyword and semantic search capabilities.
search_news: Full-text keyword search across the entire SIP archive (back to ~2012), with filters for date range, category, and interface language. Returns newest-first results with title, summary, URL, date, category, and content language.browse_latest_news: Browse the chronological news feed without a search query, supporting the same date, category, and language filters. Useful for exploring recent items or a specific date range.get_article: Fetch the full, cleaned body text of a specific article by URL, with a configurable character limit (up to 40,000 chars). Returns body text, summary, word count, category, and content language.list_categories: Retrieve reference lists of available category keys (e.g.communiques,articles,discours) and supported interface languages for use in other tools.semantic_search: Meaning-based (vector similarity) search using natural-language questions — finds relevant articles even when exact keywords don't match. Supports filters for date, category, and language, and returns a similarity score per result. Requires the semantic index to be built first.build_semantic_index: Build or incrementally update the local vector index (ChromaDB/Qdrant) by embedding article titles and summaries via an embedding model (e.g. via OpenRouter). Safe to re-run periodically to index new content.semantic_index_status: Inspect the current vector index state: item count, date range covered, content languages, embedding model in use, storage location, and API key configuration.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@SIP News MCPSearch for recent news about digital innovation in Luxembourg"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
SIP News MCP Server for Luxembourg Government News
An MCP connector that lets an AI assistant
search and read the official news of the Luxembourg government press service
(Service information et presse, SIP) at
sip.gouvernement.lu.
It exposes the full SIP news archive (press releases, communiqués, speeches, state visits, ministerial news, ~2300 items back to February 2000) as clean, structured, full-text-searchable data, in German, French or English.
Why it exists
The SIP news page loads its list through a JavaScript component, so a naive HTTP fetch returns an empty shell. This connector instead uses the site's own RSS endpoints, which return clean, paginated, newest-first data:
Purpose | Endpoint |
Browse all news |
|
Full-text search |
|
Each page returns 50 items as a sliding window over the whole archive. The connector pages through automatically, de-duplicates, decodes the (double-encoded) entities, recovers each item's category/language/date from its URL, and can fetch the full article body on demand.
Related MCP server: afpnews-mcp-server
Tools
Tool | What it does |
| Keyword full-text search across the whole archive (newest first). |
| Meaning-based (vector) search; handles natural-language questions. |
| The chronological news feed, newest first. |
| Fetch the full, cleaned text of one article by URL. |
| Reference list of category keys and interface languages. |
| (Re)build the local vector index used by |
| Size and coverage of the vector index. |
Common parameters:
language—de,froren(interface language; many items are in French regardless, so try more than one if needed).limit— how many results to return; larger values page deeper into history.since/until—YYYY-MM-DDdate bounds.category— e.g.communiques(press releases),articles,discours(speeches); seelist_categories.
Returned fields per item: title, summary, url, published (ISO),
published_human, category, category_label, content_language, source.
search_news / browse_latest_news also return count, complete and an
optional note. complete: false means the scan stopped at the page cap or
time budget rather than covering the whole archive/range, so a short or empty
list there is not an authoritative "nothing exists" (the note explains how
to narrow the query). get_article returns the body and a truncated flag.
Semantic search
semantic_search matches news by meaning rather than exact keywords, so it
answers natural-language questions ("military cooperation with Belgium",
"attacks on state IT systems") even when the wording differs from the article.
How it works:
build_semantic_indexpages through the archive, embeds each item (title + summary) with an embedding model, and stores the vectors in the configured vector store (ChromaDB by default). For NVIDIA retrieval models on OpenRouter, documents are embedded aspassageand queries asquery(asymmetric retrieval), which sharply improves ranking.semantic_searchembeds the question and returns the nearest items by cosine similarity, with the samelanguage/category/since/untilfilters. Each result carries asimilarityscore in[0, 1].
Setup:
Get an OpenRouter API key and put it in the server's environment as
OPENROUTER_API_KEY(see the config below). The default modelnvidia/llama-nemotron-embed-vl-1b-v2:freeis free. (Or use a local embedder instead, see below.)Build the index once (from Claude, call
build_semantic_index, or run the one-liner below). A full build covers the whole archive (~2300 items, back toin about a minute. Re-run it periodically to pick up new news.
uv run --extra semantic python -c "import asyncio; from sip_news_mcp.semantic import SemanticIndex; print(asyncio.run(SemanticIndex().build(language='fr', max_items=3000)))"Relevant environment variables:
Variable | Purpose |
|
|
| Embedding API key (only for the default OpenRouter provider). |
| Embedding model id (default: the free Nemotron model). |
| Embedding API base; point at a local server for self-hosted embeddings. |
| Embedding key (falls back to |
|
|
|
|
| Chroma index location; local disk only (default under |
See .env.example.
Local / self-hosted embeddings (Ollama, OpenAI-compatible)
Point SIP_NEWS_EMBED_BASE_URL at any OpenAI-compatible /embeddings server
(Ollama, vLLM, LocalAI, text-embeddings-inference, ...). No OpenRouter key is
needed. For example, with Ollama (ollama pull nomic-embed-text):
SIP_NEWS_EMBED_BASE_URL=http://localhost:11434/v1
SIP_NEWS_EMBED_MODEL=nomic-embed-text
# no key; input_type is auto-disabled for non-OpenRouter endpointsThen build the index. (Changing the embedding model/endpoint changes the vector
space, so rebuild the index with refresh=true when you switch.)
Keyword-only mode (no RAG, easiest to deploy)
Semantic search is optional. With SIP_NEWS_SEMANTIC=off (or simply by not
installing a vector backend), the server exposes only the four keyword tools
(search_news, browse_latest_news, get_article, list_categories). No
vector database, no embeddings, no OpenRouter key, and none of the heavy
optional dependencies are needed, which makes it the simplest thing to deploy.
The vector-DB dependencies are optional extras, so the base install is lightweight:
Install | What you get |
| Keyword-only (no vector deps). |
| + embedded ChromaDB backend. |
| + Qdrant backend. |
| + both backends. |
With SIP_NEWS_SEMANTIC=auto (the default) the server enables semantic search
only when the configured backend's library is actually installed.
Requirements
uv (recommended), or Python 3.10+ with
pip.
uv will download a suitable Python automatically; you do not need one
installed system-wide.
Note (Windows + network drives). If you keep this project on a network / UNC drive, Python's Windows extensions (
pywin32, pulled in bymcp) cannot load their DLLs from a UNC path, so the virtual environment must sit on a local disk. PointUV_PROJECT_ENVIRONMENTat a local folder (the project code can stay on the network drive; only the installed environment needs to be local). The commands and MCP config below set it.
Quick start
cd C:\path\to\SIP-MCP-Connector
$env:UV_PROJECT_ENVIRONMENT = "$env:LOCALAPPDATA\sip-news-mcp\venv"
uv sync --extra semantic --extra dev # LOCAL env with semantic + test deps
uv run --extra dev pytest # run the offline test suite
uv run --extra semantic sip-news-mcp # start the server (stdio, with semantic)
# For a keyword-only server, drop the extras: uv run sip-news-mcpA quick live check without an MCP client (run in the same shell, so it reuses the local environment set above):
uv run python -c "import asyncio; from sip_news_mcp.client import SipNewsClient; print(asyncio.run(SipNewsClient().search('cyber', language='de', limit=3)))"Use it from Claude
Claude Desktop
Add this to claude_desktop_config.json
(%APPDATA%\Claude\claude_desktop_config.json on Windows), then restart Claude
Desktop. See examples/claude_desktop_config.json:
{
"mcpServers": {
"sip-news": {
"command": "uv",
"args": ["--directory", "C:\\path\\to\\SIP-MCP-Connector", "run", "--extra", "semantic", "sip-news-mcp"],
"env": {
"UV_PROJECT_ENVIRONMENT": "C:\\sip-news-mcp\\venv",
"OPENROUTER_API_KEY": "sk-or-v1-...your key...",
"SIP_NEWS_CHROMA_DIR": "C:\\sip-news-mcp\\chroma"
}
}
}
}(--extra semantic and the OPENROUTER_API_KEY / SIP_NEWS_CHROMA_DIR env are
only needed for semantic search; for a keyword-only server drop them. If Claude
Desktop cannot find uv, use the absolute path to uv.exe, since it may not
inherit your shell PATH.)
Claude Code (CLI)
claude mcp add sip-news `
--env UV_PROJECT_ENVIRONMENT="$env:LOCALAPPDATA\sip-news-mcp\venv" `
--env OPENROUTER_API_KEY="sk-or-v1-...your key..." `
-- uv --directory "C:\path\to\SIP-MCP-Connector" run --extra semantic sip-news-mcpExample prompts
"Search SIP for news about cybersécurité in 2025 and summarise the top 5."
"List all SIP press releases (
communiques) since 2026-01-01.""Find SIP articles mentioning armée and open the most recent one in full."
Notes and limits
Search uses the SIP site's own full-text engine, which is a broad match: a hit may mention the term only in its body, and ranking is the site's, not ours. The connector returns those results faithfully. Use
get_articleto confirm relevance before quoting.Content language varies per item; the connector reports
content_languageper result so you can tell French items from German ones.Date filtering is most efficient for recent ranges (the feed is newest-first and stops early once it passes
since); very old ranges page deeper and may hit the page cap or the ~45s time budget, in which case the result is markedcomplete: falsewith an explanatorynote.Identical requests are cached in-process for 5 minutes (bounded LRU), so repeating the same query does not re-hit the server. A single search/browse call still issues up to ~40 sequential page requests, but they are made one at a time (each awaited before the next) under a descriptive User-Agent, which keeps load on the public government server modest.
get_articleonly fetcheshttpsURLs onsip.gouvernement.lu(host is parsed and checked, not substring-matched), so it cannot be turned into a request to other hosts.This connector only reads public pages; it performs no writes and needs no credentials.
Deployment (Docker / Kubernetes)
The server speaks two transports, chosen by MCP_TRANSPORT:
stdio(default) for Claude Desktop / Code (local subprocess).http(streamable-http) for containers and Kubernetes, listening onMCP_HOST:MCP_PORT(default0.0.0.0:8000, path/mcp).
It also supports two vector backends via SIP_NEWS_VECTOR_BACKEND:
chroma(embedded, default) for local use.qdrant(a shared, network Qdrant) for containers / multiple replicas, set withQDRANT_URL(and optionalQDRANT_API_KEY).
Docker Compose
Brings up Qdrant + the MCP server (HTTP) together:
echo "OPENROUTER_API_KEY=sk-or-v1-...your key..." > .env
docker compose up -d --build
docker compose run --rm index-build # populate the vector index once
# MCP server: http://localhost:8000/mcp (streamable-http)Minimal / keyword-only (no RAG)
The simplest deployment: no Qdrant, no embeddings, no API key. Just the four keyword tools.
docker compose -f docker-compose.minimal.yml up -d --build
# MCP server on http://localhost:8000/mcpThe minimal image is built with no vector-DB dependencies
(docker build --build-arg EXTRAS="" -t sip-news-mcp:minimal .) and runs with
SIP_NEWS_SEMANTIC=off.
Docker (image only)
docker build -t sip-news-mcp:latest . # full (RAG) image
docker run --rm -p 8000:8000 \
-e OPENROUTER_API_KEY=sk-or-v1-... \
-e SIP_NEWS_VECTOR_BACKEND=qdrant -e QDRANT_URL=http://host.docker.internal:6333 \
sip-news-mcp:latestKubernetes (Helm)
The chart in deploy/helm/sip-news-mcp deploys the
server, a bundled Qdrant (StatefulSet + PVC), a Secret for the API key, and an
optional one-shot Job that builds the index after install.
helm install sip-news deploy/helm/sip-news-mcp \
--set openrouter.apiKey=sk-or-v1-...your key...
# or reference an existing Secret: --set openrouter.existingSecret=my-secretFor a keyword-only deployment (just a Deployment + Service, no Qdrant, Secret or
Job), set semantic.enabled=false:
helm install sip-news deploy/helm/sip-news-mcp --set semantic.enabled=falseKey values (see values.yaml):
Value | Default | Purpose |
|
| Set |
|
|
|
|
| Deploy a bundled Qdrant; set |
|
| API key (creates a Secret), or use |
|
| Run a post-install Job to populate the index (qdrant backend). |
|
| Expose via an Ingress. |
|
| Scale out (qdrant backend; keep |
Reach it with kubectl port-forward svc/sip-news-... 8000:8000, then point an
MCP client at http://localhost:8000/mcp.
Connecting an MCP client over HTTP
Claude Desktop's config is stdio-only; to use the HTTP server, configure an
MCP client that supports the streamable-http transport with URL
http://<host>:8000/mcp.
Project layout
src/sip_news_mcp/
client.py HTTP client, RSS/article parsers, filtering, pagination
semantic.py OpenRouter embeddings + the SemanticIndex
vectorstore.py pluggable vector backends (ChromaDB / Qdrant)
build_index.py `sip-news-index` CLI (one-shot index build, used by jobs)
server.py FastMCP server, tool definitions, stdio/http transport
__main__.py `python -m sip_news_mcp`
tests/
test_urls.py URL / host / selector-metadata tests
test_parsing.py RSS feed + article parser tests
test_client.py SipNewsClient pagination / filtering tests
test_semantic.py vector layer tests (fake embedder)
test_server_config.py semantic on/off/auto config logic
test_server_tools.py MCP tool registration / integration (each tool >=2x)
conftest.py shared fixtures; fixtures/ captured live responses
Dockerfile container image (HTTP transport)
docker-compose.yml Qdrant + MCP server + one-shot index build
deploy/helm/sip-news-mcp/ Kubernetes Helm chart
examples/
claude_desktop_config.jsonMaintenance
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