Analysis updated 2026-05-18
Ask Claude or another AI assistant when to run your dishwasher or charge your EV based on live German electricity prices
Add a German electricity price awareness tool to a home automation AI agent
Store a rolling history of German electricity prices locally for machine learning experiments
Build a savings calculator page that compares fixed versus dynamic electricity tariffs using live SMARD data
| dasclown/strompreis-mcp | 0-bingwu-0/live-interpreter | 0xkaz/llm-governance-dashboard | |
|---|---|---|---|
| Stars | 2 | 2 | 2 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 2/5 | 2/5 | 4/5 |
| Audience | developer | general | ops devops |
Figures from each repo's GitHub metadata at analysis time.
Requires a cron job to refresh SMARD data every 15 minutes and a compatible MCP client like Claude Desktop.
Strompreis MCP is a Python package that lets AI agents like Claude or any MCP-compatible assistant access real-time German electricity price forecasts. Strompreis is German for electricity price. The data comes from SMARD, the official German electricity market data platform run by the federal network agency. A background collector fetches new prices every fifteen minutes and stores them in a local SQLite database. The MCP server reads from that database and exposes three tools. The first tool returns a price forecast for the next one to seventy-two hours as a JSON array with timestamps, prices in cents per kilowatt-hour, a confidence indicator, and a flag for peak hours. The second tool returns a plain-text recommendation of the cheapest hours within a window, phrased so an AI can read it directly back to a user. The third tool reports the health of the local database. Practical examples in the README show an AI agent answering questions like when to run the dishwasher, whether to charge an electric car now or wait, and what the forecast looks like for tomorrow. The agent calls the appropriate tool and returns a specific, time-stamped answer. Setup involves installing the package with pip, running a setup script to initialize the database and collect the first data, and adding the server to your Claude Desktop or Cline configuration. The collector should be scheduled with a cron job to refresh every fifteen minutes. The package also includes a small website component that calculates potential savings from switching to a dynamic electricity tariff. The project is licensed under MIT.
An MCP server that gives AI assistants real-time German electricity price forecasts from SMARD, letting agents recommend the cheapest time to run appliances or charge EVs.
Mainly Python. The stack also includes Python, SQLite, FastAPI.
MIT license: use, modify, and distribute freely for any purpose including commercial.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.