Corrently Regional Green Index MCP for AI. Schedule loads when power is clean AND cheap.
Works with every AI agent you already use
…and any MCP-compatible client








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Corrently Regional Green Index provides hyper-local energy intelligence, allowing you to forecast when a region's power grid is cleanest and check real-time electricity market costs.
By querying your AI agent with this MCP, you can schedule high-load tasks (like running washing machines or simulations) for times when the local mix of power is most sustainable.
What your AI can do
Get regional green index
Returns a forecast that tells you when local electricity grids are powered by the most sustainable energy mix for any given ZIP code.
Get energy market data
Retrieves the latest available data on wholesale electricity exchange prices and market trends.
Get a predicted sustainability score for local power grids based on your ZIP code.
Retrieve the latest price data from electricity exchanges, showing you what energy costs right now.
Identify specific hours when consuming power will minimize your carbon footprint relative to current grid generation.
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Corrently Regional Green Index: 2 Tools
These tools give your agent the ability to check regional energy sustainability forecasts and pull live data on electricity market pricing.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Corrently Regional Green Index on VinkiusGet Regional Green Index
Returns a forecast that tells you when local electricity grids are powered by the most sustainable energy mix for any given ZIP code.
Get Energy Market Data
Retrieves the latest available data on wholesale electricity exchange prices and...
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 2 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Today, figuring out optimal power usage is a multi-tab nightmare.
You start by checking one dashboard for regional sustainability data, only to realize you need to cross-reference that time window with a separate market pricing spreadsheet. Then, you jump to a third platform just to confirm the current price volatility. It's copy-pasting across three different tabs, comparing colored dots on a graph to numbers in a table.
With this MCP connection, your agent handles all of it. You ask for an optimal window, and it combines the green forecast with real-time market rates into one simple answer. The data comes together without you lifting a finger.
Corrently Regional Green Index: Get combined energy intelligence.
You eliminate the need to manually compare separate reports on grid cleanliness and exchange prices. Your agent integrates both `get_regional_green_index` forecasts and `get_energy_market_data` streams automatically, providing a single risk score for any given time slot.
The difference is that you move from analyzing siloed data points to generating integrated strategies. You're no longer looking at charts; you're getting decisions.
What your AI can actually do with this
This connection gives your agent deep intelligence into regional energy flows. You stop guessing about grid sustainability and start planning based on data. It tells you two things: first, how clean the electricity is right now—using a Green Power Index forecast by ZIP code; and second, what that power actually costs at the exchange rate.
Whether you're running a smart home or simulating resource-heavy tasks, your agent acts like a dedicated energy consultant. You just ask it to check the optimal timing for consumption based on regional sustainability goals and current market prices. Connecting this MCP through Vinkius lets any compatible AI client route these two data streams together, giving you actionable answers instead of raw spreadsheets.
019d842a-6cf1-727b-9895-8464d9d0ff12 Here's how it actually works
The bottom line is you get actionable energy intelligence without needing to manage multiple dashboards or APIs.
Subscribe to this MCP on Vinkius. No API key is needed because access is public.
Your AI client queries the MCP, asking for specific regional data (like a ZIP code or time frame).
The agent pulls both the Green Index forecast and current market prices, giving you a single, combined answer.
Who is this actually for?
Anyone dealing with infrastructure planning, sustainability mandates, or high-load consumption. Think of the DevOps engineer who needs to schedule large compute jobs when carbon costs are lowest, or the city planner modeling green transitions.
Schedules energy-intensive computing tasks (like model training) using your agent only during times when the regional power mix is powered by renewables.
Compares real-time electricity market prices against localized green index forecasts to identify risk periods and profit opportunities.
Automates appliance scheduling, making sure the washing machine or EV charger runs during the cleanest hours of the day.
What Changes When You Connect
You stop running heavy workloads randomly. By using the forecast from get_regional_green_index, you schedule high-demand tasks specifically for periods with maximum renewable generation, lowering your footprint.
Cost planning just got smarter. Instead of guessing what energy costs will be, calling get_energy_market_data gives you real-time pricing so you can budget infrastructure overhauls accurately.
You gain a holistic view by combining both data sources. Your agent doesn't just tell you the greenest time; it tells you if that clean power is also cost-effective right now.
Avoid unexpected costs and carbon penalties. The MCP helps pinpoint specific optimal windows, letting you plan your usage around sustainability targets and market volatility simultaneously.
Build resilient systems. You can design smart processes that automatically react to changing grid conditions, minimizing both environmental impact and operational expense.
See it in action
Optimizing Data Center Workloads
A DevOps engineer needs to run a massive data processing job. They ask their agent: 'When is the grid cleanest AND cheapest for ZIP 80331?' The agent uses get_regional_green_index and get_energy_market_data to tell them that running the job tonight between 11 PM and 2 AM maximizes renewable use while market prices are at a local low.
Planning Residential Solar Integration
A homeowner wants to buy an EV charger. They ask their agent: 'What's the best time for charging this week?' The agent checks get_regional_green_index and advises them that running the charge between 2 PM and 4 PM on Thursday maximizes solar use, regardless of minor price fluctuations.
Analyzing Regulatory Compliance
An environmental consultant must report on localized sustainability. They instruct their agent to 'Analyze regional grid performance for ZIP 10117 over the last month.' The agent uses get_regional_green_index and aggregates market data, providing a clear picture of both compliance metrics and financial risk.
Forecasting Operational Stress
An infrastructure firm needs to know if their current grid capacity is stressed. They ask the agent to 'Compare regional green index trends with recent market price spikes.' The combined data reveals that while the index looks healthy, volatile pricing suggests underlying transmission weaknesses.
The honest tradeoffs
Only checking sustainability
A developer only checks get_regional_green_index and schedules a job for 2 PM because the index is high. But market data shows that at 2 PM, prices are spiking due to an unexpected localized shortage.
Always query both tools together. Ask your agent: 'Find optimal scheduling when get_regional_green_index is above X AND get_energy_market_data indicates low price.' This balances green goals with cost reality.
Only checking market prices
An analyst only uses get_energy_market_data to find the cheapest time, say 4 AM. However, at 4 AM, the grid might be running on older, dirtier power sources, defeating sustainability goals.
You must incorporate green metrics. Ask your agent to factor in the regional index: 'Find optimal scheduling when get_energy_market_data is low AND get_regional_green_index is high.' This ensures both cost and climate targets are met.
Using old reports
Relying on quarterly PDF reports that show average energy performance for a region, missing daily spikes or dips.
Use the MCP to get live data. Your agent accesses both get_regional_green_index and get_energy_market_data directly, giving you real-time insight into today's potential.
When It Fits, When It Doesn't
Use this MCP if your core problem requires balancing two variables: environmental impact (the 'greenness') AND economic cost. For example, scheduling a high-load job or optimizing an infrastructure plan. Don't use it if you only need one thing; if you just want historical price trends without sustainability context, using a basic data retrieval tool is enough. If your goal is pure capacity planning unrelated to energy mix (e.g., counting users), this MCP won't help. But if the problem boils down to: 'When should I consume X based on both environmental metrics and current cost?', then this MCP provides the necessary combined view.
Questions you might have
What ZIP codes can I use with the get_regional_green_index tool? +
The index supports various supported ZIP codes, but you should check the Corrently documentation for the full list of regions available. The agent will confirm if your specific code is supported.
Does this MCP give me historical energy market data? +
No, this MCP focuses on current and forecasted conditions. get_energy_market_data provides the latest exchange prices, not long-term trend analysis or historical records.
How do I schedule a job using get_regional_green_index? +
Simply ask your agent to identify the best time for a specific task. You need to provide both the ZIP code and the required load profile so the MCP can calculate the optimal window.
Are there any fees when I use get_energy_market_data? +
The data provided by this MCP is free to access, meaning you don't need to worry about API keys or subscription costs for basic usage.
How do I connect my agent to the get_regional_green_index tool? +
You don't need an API key. This MCP provides public access, meaning any compatible client can query the index directly without setting up credentials.
Are there rate limits when using the get_energy_market_data tool? +
We monitor usage to ensure reliable service for all users. While we don't publish strict quotas, heavy or excessive querying patterns might encounter temporary limits.
What geographic areas does the get_regional_green_index tool cover? +
The index currently focuses on regions within Germany. It is built to provide accurate green power forecasts using German ZIP codes.
How detailed are the predictions from the get_regional_green_index tool? +
Forecasts provide time-based predictions, letting you pinpoint specific blocks when the local grid is expected to be cleanest. This helps schedule heavy loads optimally.
Does this work outside of Germany? +
The Green Power Index (GSI) is currently most accurate and comprehensive for German ZIP codes. Market data includes broader European exchange info.
What is the GSI scale? +
The Green Power Index (GSI) ranges from 0 to 100. A higher value indicates a higher percentage of renewable energy in the regional grid at that specific time.
Can I get market prices for today? +
Yes. The get_energy_market_data tool retrieves current electricity exchange prices, which are essential for industrial and commercial energy management.
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