Awattar MCP. Schedule your smart devices based on real-time energy costs.

This server hooks your AI client directly into aWATTar's real-time energy price feed, giving you granular control over how you run your house. You won't just look at prices; you'll use the data to schedule everything—from washing machines to EV charging—for when the electricity is dirt cheap. It’s all about knowing that predictive curve.

get_current_yaml: This tool retrieves aWATTar's current price statistics and operational thresholds, formatting them into a ready-to-use YAML block. You'll use this to instantly configure smart home systems. The output gives you the latest hourly rate information and any necessary limits or parameters structured exactly how controllers like LOXONE expect it.

When you run get_current_yaml, your agent gets a clean, actionable configuration file that tells other devices what the current pricing landscape is, making integration simple and immediate.

get_market_data: This tool fetches general market data points from the EPEX Spot market. You're pulling real-world electricity prices for specific time windows. By running get_market_data, you can grab two key types of information: first, general pricing that covers a 24-hour window; and second, the full forecast curve for tomorrow. This is essential because it lets you look ahead, figuring out exactly when high-consumption appliances should run to save real cash.

When you need historical context—say, you're running an efficiency report or auditing usage over a past month—you use get_market_data again. It accepts epoch timestamps, which means you can tell it the exact start and end time of any period you want to analyze. You get precise market data for that specific window, letting you generate detailed reports on energy costs over arbitrary time frames.

Because the tool handles both real-time querying and forecasting, you'll never have to guess what your electricity costs are next week. By pulling tomorrow's predicted price curve, you can program complex routines: if prices dip below a certain threshold (which you get from get_current_yaml), then run the water softener; if they climb too high, then pause the main HVAC unit until the cost drops.

It’s about making sure your heavy usage aligns perfectly with the lowest price points available.

You're not just checking a dashboard here. You're building an automated system that reacts to market forces. The combination of getting instant YAML configs for immediate actions and pulling detailed, time-specific historical data via epoch timestamps gives you complete control over consumption planning. It’s smart automation based on hard numbers, period.