Skip to main content
Glama
schlpbch

Aareguru MCP Server

by schlpbch
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Aareguru MCP Server

FastMCP Cloud Tests Coverage Python FastMCP Version

MCP server for Swiss Aare river data, enabling AI assistants like Claude to answer questions about swimming conditions, water temperature, flow rates, and safety.

Latest Release: v4.2.0 - Service layer pattern (ADR-014) and FastMCP Cloud deployment formalization (ADR-015). All 15 ADRs now accepted. See CHANGELOG.md for details.

🚀 Quick Start

Use directly from FastMCP Cloud (no installation needed):

Add it is as custom connector in Claude Desktop:

Claude Custom Connector

No authentication is needed.

Altnernatively, you can add the aareguru-mcp.mcpb file via option in Claude -> Settings -> Extensions. Or edit the Claude desktop config file directly:

{ "mcpServers": { "aareguru": { "url": "https://aareguru.fastmcp.app/mcp" } } }

📸 Screenshots

Claude Desktop Integration Claude Desktop Integration Claude Desktop Integration

🎯 Features

Feature

Description

6 MCP Tools

Temperature, flow, safety, forecasts, history, comparisons

3 MCP Resources

Direct data access via aareguru:// URIs

3 MCP Prompts

Daily reports, spot comparisons, weekly trends

Rate Limiting

100 req/min, 1000 req/hour protection against abuse

Metrics

Prometheus endpoint for monitoring and observability

Swiss German

Authentic temperature descriptions ("geil aber chli chalt")

BAFU Safety

Official flow danger levels and thresholds

Smart UX

Proactive safety warnings, alternative suggestions, seasonal context

210 Tests

87% coverage, comprehensive test suite

Async-First

Context managers, parallel API fetching with asyncio.gather()

🛠️ Tools

Tool

Description

Example Query

get_current_temperature

Water temperature with Swiss German text

"What's the Aare temperature?"

get_current_conditions

Full conditions (temp, flow, weather)

"How's the Aare looking today?"

get_flow_danger_level

Flow rate + BAFU safety assessment

"Is it safe to swim?"

compare_cities

Compare all cities (parallel fetching)

"Which city is warmest?"

get_forecasts

Forecasts for multiple cities (parallel)

"Show forecasts for all cities"

get_historical_data

Temperature/flow history (hourly data)

"Show last 7 days for Bern"

BAFU Safety Thresholds

Flow Rate

Level

Status

< 100 m³/s

Safe

Swimming OK

100-220 m³/s

Moderate

Experienced swimmers only

220-300 m³/s

Elevated

Caution advised

300-430 m³/s

High

Dangerous

> 430 m³/s

Very High

Extremely dangerous

📊 Resources

URI

Description

aareguru://cities

List of all monitored cities

aareguru://current/{city}

Full current data for a city

aareguru://today/{city}

Minimal current data

💬 Prompts

Prompt

Description

daily_swimming_report

Comprehensive daily report with conditions, safety, forecast, and recommendation

compare_swimming_spots

Compare all cities to find the best swimming spot today

weekly_trend_analysis

Analyze temperature and flow trends over the past week

💻 Local Installation

# Install uv and clone curl -LsSf https://astral.sh/uv/install.sh | sh git clone https://github.com/schlpbch/aareguru-mcp.git && cd aareguru-mcp uv sync # Run tests uv run pytest

Claude Desktop (Local)

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{ "mcpServers": { "aareguru": { "command": "uv", "args": ["--directory", "/path/to/aareguru-mcp", "run", "aareguru-mcp"] } } }

🐳 Docker

cp .env.example .env docker-compose up -d curl http://localhost:8000/health

☁️ Hosting

FastMCP Cloud (Recommended)

This server is deployed on FastMCP Cloud, a managed platform for MCP servers with zero-config deployment.

Features:

  • ✅ Zero-Config Deployment - Connect GitHub repo, automatic deployment

  • ✅ Serverless Scaling - Scale from 0 to millions of requests instantly

  • ✅ Git-Native CI/CD - Auto-deploy on push to main, branch deployments for PRs

  • ✅ Built-in Security - OAuth support, token management, secure endpoints

  • ✅ MCP Analytics - Request/response tracking, tool usage insights

  • ✅ Free Tier - Available for personal servers

Deployment Steps:

  1. Sign in to fastmcp.cloud with GitHub

  2. Create Project and link your repository

  3. Deploy - Platform automatically clones, builds, and deploys

  4. Access - Get your unique URL (e.g., https://aareguru.fastmcp.app/mcp)

Configuration:

No special configuration needed! FastMCP Cloud auto-detects FastMCP servers. The server runs with:

  • Health endpoint: https://your-app.fastmcp.app/health

  • MCP endpoint: https://your-app.fastmcp.app/mcp

Pricing:

  • Free tier for personal projects

  • Pay-as-you-go for teams (usage-based)

Alternative Hosting Options

FastMCP servers can be deployed to any Python-compatible cloud platform:

Container Platforms:

  • Google Cloud Run

  • AWS ECS/Fargate

  • Azure Container Instances

PaaS Providers:

  • Railway

  • Render

  • Vercel

Cloud VMs:

  • AWS EC2

  • Google Compute Engine

  • Azure VMs

Deployment Pattern:

# For HTTP deployment, modify server to use HTTP transport from fastmcp import FastMCP mcp = FastMCP("aareguru") # ... register tools ... if __name__ == "__main__": mcp.run(transport="sse") # Server-Sent Events for HTTP

Then containerize with Docker and deploy to your chosen platform.

📊 Monitoring & Observability

Prometheus Metrics

The server exposes Prometheus-compatible metrics at /metrics for monitoring:

Available Metrics:

  • aareguru_mcp_tool_calls_total - Counter of tool invocations by name and status

  • aareguru_mcp_tool_duration_seconds - Histogram of tool execution times

  • aareguru_mcp_api_requests_total - Counter of Aareguru API requests

  • aareguru_mcp_errors_total - Counter of errors by type and component

  • aareguru_mcp_active_requests - Gauge of currently active requests

Example:

curl http://localhost:8000/metrics

Rate Limiting

HTTP endpoints are protected with rate limiting:

  • Default limits: 100 requests/minute, 1000 requests/hour

  • Health endpoint: 60 requests/minute

  • Headers: Rate limit info included in responses

  • 429 responses: Automatic retry-after headers when limits exceeded

🧪 Development

uv run pytest # Run tests (210 tests) uv run pytest --cov=aareguru_mcp # With coverage (87%) uv run black src/ tests/ # Format uv run ruff check src/ tests/ # Lint

📖 Documentation

  • ARCHITECTURE.md - Comprehensive architecture guide (design patterns, data flow, deployment)

  • DEPLOYMENT.md - FastMCP Cloud deployment guide with monitoring and troubleshooting

  • CHANGELOG.md - Release history and version details

  • CLAUDE.md - Development guide for AI assistants

  • specs/ADR_COMPENDIUM.md - Architecture Decision Records (15 ADRs, all accepted)

  • docs/ - Additional API documentation and planning

📁 Project Structure

aareguru-mcp/ ├── src/aareguru_mcp/ # Server, client, models, config ├── tests/ # 210 tests, 87% coverage ├── docs/ # API docs, testing, implementation ├── ARCHITECTURE.md # Comprehensive architecture documentation ├── CLAUDE.md # AI assistant guidance ├── CHANGELOG.md # Release history ├── mcp_server.py # FastMCP CLI entry └── pyproject.toml

🔒 Data Attribution

Data from BAFU, Aare.guru, MeteoSchweiz, Meteotest.

Non-commercial use only - Contact: aaregurus@existenz.ch

📄 License

MIT License - See LICENSE

Built with ❤️ for the Swiss Aare swimming community

Install Server
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

How are these scores calculated?

Resources

Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/schlpbch/aareguru-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server