Swiss Environment Mcp

🇨🇭 Part of the Swiss Public Data MCP Portfolio

🌿 swiss-environment-mcp

[Version] [License: MIT] [Python 3.11+] [MCP] [CI] [Data Source]

MCP server connecting AI models to Swiss environmental data from BAFU – air quality, hydrology, natural hazards, wildfire danger and open environmental datasets.

🇩🇪 Deutsche Version

[Demo: Claude queries NABEL air quality via a swiss-environment-mcp tool call and gets a WHO 2021 compliance check]

Overview

swiss-environment-mcp gives AI assistants like Claude direct access to real-time environmental data from Swiss federal authorities – no API keys required. Air quality readings from the national NABEL monitoring network, hydrological gauging stations, natural hazard bulletins, and the full BAFU dataset catalogue are all accessible through a single standardised MCP interface.

The server covers four thematic clusters: air quality (NABEL), hydrology, natural hazards, and the BAFU open data catalogue. Each cluster maps to a group of purpose-built tools that translate raw agency data into clean JSON responses.

Anchor demo query: "What is the current air quality at the NABEL station Zürich-Kaserne – and does it comply with WHO 2021 guidelines?" → More use cases by audience →

Features

• 🌬️ Air quality monitoring – 16 NABEL stations, NO₂/O₃/PM10/PM2.5/SO₂/CO, Swiss LRV + WHO 2021 limit checks
• 💧 Hydrology – water levels, flow rates, temperatures across Swiss gauging stations
• 🚨 Flood warnings – active alerts filtered by danger level and canton
• 🏔️ Natural hazard bulletin – SLF/BAFU bulletin in DE/FR/IT/EN, region-specific warnings
• 🔥 Wildfire danger – canton- and region-level fire danger index
• ❄️ Snow & avalanches (SLF) – snow depth, new snow per IMIS station; avalanche danger levels (EAWS)
• 🦌 Hunting statistics – cull & game-loss figures per species, canton and year (federal hunting statistics)
• 📦 BAFU open data catalogue – search and retrieve environmental datasets via CKAN
• 🔑 No authentication required – all data sources are publicly accessible
• ☁️ Dual transport – stdio for Claude Desktop, Streamable HTTP/SSE for cloud deployment

Prerequisites

• Python 3.11+
• No API keys needed – all endpoints are publicly accessible without authentication

Installation

# Clone the repositorygit clone https://github.com/malkreide/swiss-environment-mcp.gitcd swiss-environment-mcp
# Installpip install -e .

Or with uvx (no permanent installation):

uvx swiss-environment-mcp

Or via pip:

pip install swiss-environment-mcp

Quickstart

# Start the server (stdio mode for Claude Desktop)swiss-environment-mcp

Try it immediately in Claude Desktop:

"What is the current air quality at NABEL station Zürich-Kaserne?" "Are there any active flood warnings in Switzerland right now?" "What is the wildfire danger level in Canton Valais?"

Configuration

Claude Desktop

Minimal (recommended):

{  "mcpServers": {    "swiss-environment": {      "command": "uvx",      "args": ["swiss-environment-mcp"],      "env": {}    }  }}

Config file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

After saving, restart Claude Desktop completely.

Cloud Deployment (SSE for browser access)

For use via claude.ai in the browser (e.g. on managed workstations without local software):

Render.com (recommended):

1. Push/fork the repository to GitHub
2. On render.com: New Web Service → connect GitHub repo
3. Render detects render.yaml automatically
4. In claude.ai under Settings → MCP Servers, add: https://your-app.onrender.com/sse

Docker:

docker build -t swiss-environment-mcp .docker run -p 8000:8000 swiss-environment-mcp

💡 "stdio for the developer laptop, SSE for the browser."

Available Tools

All tools share the stable env_ name prefix — a deliberate namespace choice so that the server's tools are recognisable and unlikely to collide when several MCP servers are mounted together. Tool definitions (name, description, input schema) are pinned via tool-snapshot.json; changes require a CHANGELOG entry (see CONTRIBUTING).

Tool budget (21 tools, 7 clusters) — exhausted. Every tool maps to a distinct user question, not to a REST endpoint — there is no CRUD/endpoint mirroring, and the anchor queries are each answerable in a single call. The count sits above the ≤12 rule of thumb because the server deliberately spans seven environmental domains (air, water, hazards, snow, hunting, catalogue, aircraft noise), each needing a list/detail pair or a domain-specific action. Further consolidation was considered and rejected: the *_stations/*_current pairs (NABEL, hydro, snow) serve genuinely different intents (discovery vs. reading a known station) and collapsing them would overload a single tool's parameters.

This is the ceiling. With the aircraft-noise cluster the server's tool budget is spent: any further data source belongs in a separate *-mcp server, not here. The next addition would instead trigger a review of whether some listings should migrate to MCP resources rather than tools.

🌬️ Air Quality / NABEL (3 tools)

💧 Hydrology (5 tools)

🏔️ Natural Hazards (3 tools)

❄️ Snow & Avalanches / SLF (3 tools)

🦌 Hunting & Wildlife (2 tools)

📊 Environmental Data Catalogue (2 tools)

✈️ Aircraft Noise / BAZL noise cadastre (3 tools)

⚖️ Legal notice — carried in every response of these three tools. The noise cadastre is an orientation aid. Legally binding information on construction projects is issued by the competent cantonal office or by the FOCA (BAZL). These tools do not replace a building permit clarification.

Coordinates must be LV95 (EPSG:2056, metres: E ≈ 2'480'000–2'840'000, N ≈ 1'070'000–1'300'000). WGS84 degrees such as 8.54 / 47.37 are rejected fail-fast with a conversion hint — this is the single most common LLM error with Swiss geodata. Convert via swisstopo REFRAME or convert_coordinates in swisstopo-mcp.

The contours are lines, not areas. The cadastre publishes MultiLineString isolines, so identify performs a proximity query within a search radius — not a point-in-polygon test. env_noise_aircraft_at therefore returns a bracket («the point lies between the 61 dB and the 62 dB contour») with the highest value as a stated upper bound, never an interpolated point value. The search radius is reported in every response; enlarging it inflates the result (at one Kloten point: 100 m → 61–62 dB, but 500 m → 58–75 dB, because the 75 dB runway contour sits 1.5 km away).

Anchor demo query

"Is the planned school site inside an aircraft-noise zone with building restrictions — and at which dB level?"

Cross-server flow: resolve an address or EGID via swiss-housing-mcp → convert to an LV95 coordinate → env_noise_aircraft_at(east=…, north=…, period="day") → feed the resulting level_db into env_noise_limits_check(level_db=…, sensitivity_level="II", period="day") for the legal classification.

swiss-housing-mcp  →  address / EGID  →  LV95 E/N                                          ↓                          env_noise_aircraft_at   → 62 dB (upper bound, LBK Zürich, valid from 03.07.2015)                                          ↓                          env_noise_limits_check  → immission limit ES II (60 dB) exceeded by 2 dB

Example Use Cases

🛡️ Safety & Limits

Architecture

┌─────────────────┐     ┌───────────────────────────┐     ┌──────────────────────────┐│   Claude / AI   │────▶│   Swiss Environment MCP   │────▶│  Upstreams (contacted)   ││   (MCP Host)    │◀────│   (MCP Server)            │◀────│                          │└─────────────────┘     │                           │     │  lindas.admin.ch (SPARQL)│                        │  21 Tools · 3 Resources   │     │  measurement-api.slf.ch  │                        │  Stdio | SSE              │     │  aws.slf.ch              │                        │                           │     │  waldbrandgefahr.ch      │                        │  api_client.py            │     │  jagdstatistik.ch        │                        │  server.py (FastMCP)      │     │  api3.geo.admin.ch       │                        └───────────────────────────┘     │  opendata.swiss (CKAN)   │                                                          └──────────────────────────┘

Architecture note — extractable lindas/ module

All LINDAS SPARQL access goes through the deliberately extractable src/swiss_environment_mcp/lindas/ module, built as three strict layers: client.py knows only SPARQL and HTTP (GET/POST, 45 s client-side timeout, QueryError carrying the server's MALFORMED message, 2 s/4 s/8 s retry); cube.py knows the cube.link vocabulary (mandatory observationSet two-phase access, version deduplication via schema:expires, code→label resolution, licence lookup); the tools only ever call cube.py. The module will be lifted into a shared lindas-mcp as soon as a second server uses LINDAS (candidate: wsl-envidat-mcp).

Architecture decision — live API for the aircraft-noise cadastre

The rest of this server follows the portfolio's dump-first standard. The aircraft-noise cluster deliberately deviates: env_noise_aircraft_at and env_noise_aircraft_registers query api3.geo.admin.ch live on every call.

This is not a size argument. Measured on 2026-07-28, the entire cadastre is 747 objects (~3 MB of GeoJSON) across all eight sublayers — it would mirror without difficulty. Two other reasons decide it:

1. A mirror would falsify the freshness claim. The cadastres are revised per airfield, individually and without announcement (validity dates 2009–2024). Read from a dump, source_freshness would report the validfrom as of the mirroring date — the tool would assert provenance it no longer has. For a cluster whose third tool exists precisely to answer "how old is the basis", that is the worst possible failure mode.
2. The spatial query is the value, not the attributes. Evaluated locally it means point-to-line distances over 26'000+ vertices per layer. That requires either shapely/GEOS — the first compiled dependency in a deliberately binary-free pyproject.toml (Docker image, wheel matrix and security surface all change) — or hand-rolled distance maths. The latter is feasible, since LV95 is a metric projection and Euclidean distance is exact; but then this server would own the correctness of an official noise-cadastre statement instead of the federal office.

Full measurements and reasoning: docs/probe-fluglaerm.md.

Data Sources

All data: publicly accessible, no authentication required.
Attribution required: BAFU / SLF (WSL) must be cited as the source when using their data.

Project Structure

swiss-environment-mcp/├── src/swiss_environment_mcp/│   ├── __init__.py          # Package│   ├── server.py            # FastMCP server: 21 tools, 3 resources│   ├── api_client.py        # HTTP client + egress allow-list (SSRF guard)│   └── logging_setup.py     # structlog -> stderr├── tests/│   ├── test_unit.py         # Mocked unit tests (no network) — CI default│   ├── test_integration.py  # Live API tests (marker: live)│   └── test_20_scenarios.py # Live scenario coverage├── scripts/tool_snapshot.py # Tool-definition hash snapshot (rug-pull guard)├── docs/                    # security.md, scaling.md, roadmap.md├── .github/│   ├── dependabot.yml       # Monthly dependency/action updates│   └── workflows/           # ci.yml, security.yml (gitleaks), live-tests.yml, publish.yml├── Dockerfile               # Multi-stage, non-root container├── render.yaml / Procfile   # Cloud deployment├── tool-snapshot.json       # Committed tool-definition snapshot├── .env.example             # Non-secret config template└── pyproject.toml           # Build configuration (hatchling)

Single-module layout (rationale, audit ARCH-011): the 21 tools live in one server.py rather than a tools/ package. They are thin, uniform wrappers over api_client.py sharing the same input/response patterns, so a single well-sectioned module stays more navigable than 4 near-identical files. This is a deliberate, documented deviation; a split is revisited if tool logic grows non-uniform.

MCP Protocol Version & Maintenance

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.

That pin measures the SDK, though, not this server: it would stay green even if the server did not answer the modern era at all. The measured half lives in tests/test_modern_protocol.py — real requests in both eras through the production app (build_cors_app(), the same one uvicorn starts in operation), with negative controls: a missing _meta envelope, a mismatched routing header, an initialize on a modern connection.

What 2026-07-28 actually means for this server

• No session, no initialize — and so no serverInfo in a handshake result. The server introduces itself through server/discover and the io.modelcontextprotocol/serverInfo _meta stamp carried by every response. Name, title, description, website and version come from the MCPServer constructor; the version from package metadata, never a literal. Without it the server reports version: "", and a stateless client cannot tell two releases apart.
• Routing headers. Mcp-Method, Mcp-Name and MCP-Protocol-Version must match the request body, or the request is rejected with -32020. That is why they appear in CORS_ALLOW_HEADERS — a browser that may not send them gets a rejection, not an answer.
• Logging is a per-request opt-in (SEP-2577). The capability is gone; without the reserved io.modelcontextprotocol/logLevel _meta key the server sends nothing. With logLevel: "warning" the response switches to text/event-stream and carries the entry. Both directions are measured.
• Freshness hints (SEP-2549): the listing methods carry ttlMs and cacheScope — see CACHE_HINTS.

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.

• Tool-definition stability (audit SEC-022): any change to a tool's name, description or parameters changes tool-snapshot.json; CI fails until the snapshot is regenerated and a CHANGELOG entry + version bump are added.
• Update policy: review Dependabot PRs monthly; bump the version (semver) on any tool-definition or behaviour change.

Lifecycle Phase

This server is in Phase 1 (read-only) — all tools read-only, no auth, no side effects. The phase model and prerequisites for Phase 2 (write/auth) are in docs/roadmap.md. Security architecture (SSRF/egress, secret management, lethal-trifecta assessment): docs/security.md. Scaling/session strategy: docs/scaling.md.

Known Limitations

• Hydrology via LINDAS: env_hydro_current, env_hydro_stations and env_flood_warnings query the BAFU LINDAS SPARQL endpoint (typed live values: level, discharge, water temperature, danger level). LINDAS holds current values only (one observation per station) — it is not a historical time series. See docs/probe-lindas-hydro.md.
• Historical hydrology / env_hydro_history (BUG-01 resolved): the old hydrodaten.admin.ch/lhg/az/* REST endpoints (hourly CSV, warnings.json, station JSON) are decommissioned (404). env_flood_warnings now uses LINDAS dangerLevel instead. Real historical time series (daily / long-term means — e.g. summer 2024 vs. long-term average) are not freely available via API; they must be ordered from the BAFU Hydrological Enquiry Service ([email protected]). env_hydro_history returns the latest LINDAS value plus this access path.
• Flood warnings: env_flood_warnings reads LINDAS dangerLevel. A canton filter is not available there (LINDAS carries no canton code), so the response is always nationwide; a canton value is echoed back and flagged as not applied — in the JSON envelope via match_type: "fuzzy" plus a note, in Markdown as a warning line above the table. This matters most when there are no warnings at all: „no active warnings" plus a canton in the request otherwise reads as an all-clear for that canton.
• Gauging stations / canton filter: env_hydro_stations no longer serves canton. The only source that carried the canton code — hydrodaten.admin.ch/lhg/az/json/mobile_stations.json — is decommissioned (404), and LINDAS has no canton attribute. A request with canton set returns an explanation instead of a station list; use water_body, or fetch the full list (233 stations) unfiltered.
• Bathing water quality (env_bathing_water): reads the LINDAS data cube foen/ubd01041prod — the only hydro cube with a real multi-year time series (seasonal samples since 2020). Data is refreshed annually after the bathing season (no real-time monitoring), and the survey covers only the officially reported bathing sites (many popular lidos are not part of it). The licence is declared at graph/dataset level, not on the cube; every response therefore carries an explicit licence field — with an honest «not declared» note where none exists. See docs/probe-lindas-hydro.md (addendum N1–N7).
• No groundwater data in LINDAS: verified 2026-07-24 via multilingual cube search — LINDAS contains no groundwater cube (NAQUA groundwater levels are not available via SPARQL).
• NABEL: Near-real-time data only; no historical time series via this server.
• Natural hazards (env_hazard_overview / env_hazard_regions): the former naturgefahren.ch/api/v1/warnings/* REST endpoints were decommissioned (2026) and — verified 2026-07-26 — there is no stable, documented public JSON feed for the aggregated warnings (MeteoSwiss OGD/STAC, opendata.swiss and the undocumented app API were all checked). Rather than a fragile scrape, both tools are now network-free orientation/router tools: they deterministically point to this server's dedicated live tools (flood→env_flood_warnings, avalanche→env_avalanche_bulletin, wildfire→env_wildfire_danger, snow→env_snow_current) and the official portals. Aggregated weather warnings (storm/thunderstorm/heat) are MeteoSwiss's domain and belong to meteoswiss-mcp. See docs/probe-naturgefahren-hazards.md.
• Wildfire danger (env_wildfire_danger): waldbrandgefahr.ch replaced its REST API with a Rails/React app in 2026; there is no stable JSON endpoint. Current danger levels are read via a two-step, HTML-borne contract: the homepage's data-react-props yields a signed ActiveStorage blob URL (warnMapJsonPath) plus the canton mapping, which is then fetched. A schema-guard degrades gracefully if that structure changes. Unfiltered results are capped at 40 regions (highest levels first); filter by canton for a full cantonal list. See docs/probe-naturgefahren-waldbrand.md.
• Hunting statistics (env_hunting_stats): The jagdstatistik.ch backend is undocumented (a content-negotiated web-app endpoint). A schema-guard degrades gracefully if the structure changes. Species/canton/datatype lookups are embedded (harvested 2026-07-19); figures are fetched live for 2015–2024. Licence (researched 2026-07-19): the data is owned by BAFU (compiled from cantonal offices; site tech by Wildtier Schweiz) and is not published as a licensed dataset on opendata.swiss; no explicit licence is stated on the source. Responses therefore require source attribution to BAFU; formal licence confirmation from BAFU is still pending. See docs/probe-jagdstatistik.md.
• Road and railway noise are out of scope (ch.bafu.laerm-*, ch.bav.laermbelastung-*): verified 2026-07-28. The BAFU road-noise layers ch.bafu.laerm-strassenlaerm_tag / _nacht answer the same identify request with HTTP 400 — they are pure raster services (type: wmts, tooltip: false) with no attribute query, so a point lookup is technically impossible. Railway noise is different: ch.bav.laermbelastung-eisenbahn_* does answer with HTTP 200 and real attributes (de_es, de_pointofdetermination), so it is queryable but deliberately not connected — the server's tool budget is exhausted at 21 and rail noise would need its own period/attribute model. Mnemonic: aircraft noise has lines, road noise has only pixels — rail noise would have data, but is a conscious omission. See docs/probe-fluglaerm.md.
• Aircraft noise is a cut-off-date cadastre, not a live service (env_noise_*): validfrom ranges from 01.03.2009 (CDB Genève) to 16.04.2024 (LBK St. Gallen-Altenrhein), and each airfield is revised individually and without notice. source_freshness therefore never claims "live" — it carries the validfrom of the register actually matched. Coverage is limited to the surroundings of airfields; most of Switzerland has no cadastre at all, which the tool reports explicitly rather than returning an empty list. A zero-hit result at a small radius is ambiguous (outside any cadastre or inside the innermost contour — on the Kloten runway both look identical at 100 m), so the tool re-queries once at a far radius and distinguishes no_cadastre from wide_area_only.
• LSV limit check excludes military airfields: Annex 5 of the Noise Abatement Ordinance applies expressly to civil airfields. For military airfields Annex 8 governs; it was not verified, so env_noise_limits_check refuses the check for period="military" and points to the correct basis instead of applying a plausible-looking wrong table.

Responsibility matrix — water, snow & precipitation (delineation vs. meteoswiss-mcp)

To avoid duplicating water, snow and precipitation data across the portfolio, responsibilities are split as follows. meteoswiss-mcp owns atmospheric precipitation and weather; swiss-environment-mcp owns surface waters (BAFU domain: discharge, water level, water temperature, bathing quality), snow on the ground and avalanche danger. Checked against the actual LINDAS cube dimensions (2026-07-24): there is no overlap in measured quantities.

Rule: everything in and on water bodies (discharge, level, water temperature, bathing quality), snow on the ground and avalanche danger belong to swiss-environment-mcp (BAFU/SLF); atmospheric precipitation (rain/snowfall as mm) plus weather, forecast, warnings and climate normals belong to meteoswiss-mcp. The SLF IMIS precipitation endpoint (RR_10MIN_SUM) is deliberately not wired up as a tool, so it does not duplicate MeteoSwiss. The snow/avalanche tools are live (see docs/probe-slf.md). TODO (out of scope here): mirror this matrix in the meteoswiss-mcp README — that repository is not part of this change.

Testing

# Unit tests (no API keys or network required)PYTHONPATH=src pytest tests/ -m "not live"
# Integration tests (requires live BAFU APIs)PYTHONPATH=src pytest tests/ -m "live"
# Lintingruff check src/

Contributing

See CONTRIBUTING.md (English) · CONTRIBUTING.de.md (German)

Security

Security policy and posture: SECURITY.md (English) · SECURITY.de.md (German). Full security architecture: docs/security.md.

Changelog

See CHANGELOG.md

License

MIT License — see LICENSE

Source data is subject to BAFU terms of use. Attribution to BAFU is required when using their data.

Author

Hayal Oezkan · github.com/malkreide

Credits & Related Projects

• Data: BAFU / Bundesamt für Umwelt · hydrodaten.admin.ch · naturgefahren.ch · opendata.swiss
• Protocol: Model Context Protocol – Anthropic / Linux Foundation
• Related:

Synergy example: "What was the air quality at Schulhaus Leutschenbach today – and how does it compare to the national NABEL average?"
→ zurich-opendata-mcp (OSTLUFT, local) + swiss-environment-mcp (NABEL, national)

• Portfolio: Swiss Public Data MCP Portfolio

Installation

Run via uv's uvx — no clone or manual install needed. Add to your MCP client config (mcpServers for Claude Desktop, Cursor and Windsurf; use a top-level servers key for VS Code in .vscode/mcp.json):

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