Supercharge your AI with Ember Climate. Analyze global energy flow and grid performance.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Ember Climate connects your AI client to a massive, open dataset covering global electricity grids. You can instantly pull data on generation mix by source (coal, wind, solar), track demand trends, monitor emissions, and measure clean energy capacity across over 200 countries in both monthly and yearly detail.
It's the global view of the energy transition.
What your AI can do
Get carbon intensity monthly
Gets the monthly carbon intensity of electricity generation for specific countries or regions using start and end dates.
Get carbon intensity yearly
Retrieves yearly carbon intensity data, showing how clean or polluting a country's grid was over an entire year.
Get electricity demand monthly
Gathers monthly electricity demand for countries, useful for spotting seasonal peak consumption periods and forecasting usage.
Determine the carbon intensity and total CO2 emissions for national electricity grids.
Break down how much power different sources, like wind or gas, contribute to a country's total generation output.
Analyze electricity consumption patterns and predict peak load periods across different nations over time.
Query several countries simultaneously to run comparative reports on any metric, like renewable adoption rates.
Track the installation and capacity of clean energy sources like solar and wind power monthly.
Ask an AI about this
Compatible AI Apps
OAuth 2.0 CompatibleWaiting for input…
Ember Climate: 11 Energy Data Tools
Use these tools to calculate carbon intensity, model demand, and track generation data across various regions and time periods.
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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 Ember Climate on VinkiusGet Carbon Intensity Monthly
Gets the monthly carbon intensity of electricity generation for specific countries or regions using start and end dates.
Get Carbon Intensity Yearly
Retrieves yearly carbon intensity data, showing how clean or polluting a country's...
Get Electricity Demand Monthly
Gathers monthly electricity demand for countries, useful for spotting seasonal peak...
Get Electricity Demand Yearly
Provides yearly electricity demand data, helping compare per-capita energy use...
Get Electricity Generation Monthly
Returns monthly electricity generation amounts and percentage shares, broken down by...
Get Electricity Generation Yearly
Provides yearly data on total power generated by various sources, critical for tracking long-term energy transition trends.
Get Generation Multi Entity
Gathers electricity generation data across multiple countries simultaneously, making comparative analysis highly efficient.
Get Installed Capacity Monthly
Tracks the monthly installed power capacity for renewable sources like wind and...
Get Api Options
Checks the API options to find valid country codes, energy source types, and...
Get Power Sector Emissions Monthly
Retrieves monthly CO2 emissions from the power sector, allowing granular tracking of...
Get Power Sector Emissions Yearly
Tracks yearly national decarbonization progress by reporting total CO2 emissions...
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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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 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with Global Energy Data Used To Be a Nightmare.
Today, pulling global energy data means jumping between government websites, downloading dozens of disparate CSV files, and then spending hours cleaning them up just to get a single comparison. You're constantly cross-referencing spreadsheets, merging incompatible date formats, and praying the source data covers the same metrics for every country.
With this MCP, your agent handles the whole process. Instead of manual aggregation, you ask one question—like 'What was the overall emissions trend in Asia?'—and get a structured answer immediately. You don't deal with files; you deal with insights.
Get Accurate Energy Data Using Ember Climate MCP
The manual steps that vanish are the data gathering, the cleaning, and the comparison setup. You don't need to write complex data pipelines just to figure out if a country is actually reducing its carbon footprint.
Now, you can model energy transitions with confidence. This isn't just about fetching numbers; it's about getting authoritative context on global climate progress.
What your AI can actually do with this
Forget manually downloading CSV files or wrestling with spreadsheets that only cover one country at a time. This MCP gives your AI client direct access to deep, real-time intelligence on the world’s power grids. You can ask complex questions—like how much Germany's coal use dropped compared to China's wind capacity growth over three years—and get structured data back immediately.
It tracks everything: total electricity demand (TWh), carbon intensity per kilowatt-hour (gCO2/kWh), and the exact breakdown of generation by source, whether you need a yearly overview or a monthly deep dive for seasonal effects. Because this MCP is hosted on Vinkius, it lets your agent connect to all global energy data sources in one place, letting you focus purely on analysis instead of API calls.
019d758f-9631-70da-beab-6f099b92c9cb Here's how it actually works
The bottom line is that your AI agent handles all the complex API calls; you just write the question.
Subscribe to this MCP on Vinkius, then input your free Ember Climate API Key.
Your agent uses a simple filter discovery tool to check for available countries, sources, and date ranges.
Finally, you prompt your AI client with the specific comparison or data point you need (e.g., 'Compare US vs. Brazil generation mix in 2023').
Who is this actually for?
Anyone who has to aggregate energy data from multiple sources or jurisdictions needs this. It's for climate researchers tired of writing custom ETL pipelines, and consultants who need real-time comparative metrics.
Runs longitudinal studies comparing global carbon intensity changes over decades, requiring both yearly and monthly data sets.
Compares the current generation mix of two competing nations (e.g., US vs. EU) to advise clients on decarbonization paths.
Fact-checks policy claims by pulling authoritative data on power sector emissions and capacity growth for specific legislative years.
What Changes When You Connect
You can analyze seasonal shifts using get_electricity_demand_monthly, seeing exactly where a region's peak power use happens each year. This is key for infrastructure planning.
Compare multiple nations in one go using get_generation_multi_entity; you don't need to run 10 separate API calls just to compare BRICS+ energy mixes.
Track clean energy deployment directly with get_installed_capacity_monthly, measuring how fast solar and wind infrastructure is actually growing country by country.
Understand long-term climate shifts using get_carbon_intensity_yearly, tracking if a national grid's average pollution (gCO2/kWh) is trending up or down over decades.
Get the full picture of power sector emissions with get_power_sector_emissions_monthly and get_power_sector_emissions_yearly, letting you contextualize corporate ESG goals against official benchmarks.
See it in action
Determining Global Decarbonization Benchmarks
A policy analyst needs to track the average pollution reduction of major economies. They use get_carbon_intensity_yearly and compare it across several countries over 15 years, immediately flagging nations that have slowed their transition.
Forecasting Peak Power Needs
An energy consultant must advise a client on grid upgrades. They use get_electricity_demand_monthly to spot the difference between peak winter load and low summer usage, ensuring infrastructure investment is correctly sized.
Assessing Renewable Adoption Speed
A researcher wants to measure how quickly solar power is replacing coal. They combine get_electricity_generation_monthly (for source breakdown) with get_installed_capacity_monthly to show both current output and future potential.
Comparing Emerging Economies
A financial analyst needs quick comparisons of multiple markets. They use get_generation_multi_entity, inputting several developing country codes at once to compare total generation mix against a baseline like the US.
The honest tradeoffs
Treating data as one giant sheet
Trying to query all metrics—demand, emissions, and capacity—in a single, massive call because it seems simpler.
You must use the specialized tools. For instance, get demand trends with get_electricity_demand_yearly, but track clean energy growth separately using get_installed_capacity_monthly.
Skipping granularity checks
Assuming that a single 'total generation' metric is enough for reporting on climate impact.
Always check the source breakdown. Use get_electricity_generation_yearly to see if coal was phased out, or use get_power_sector_emissions_yearly to confirm the CO2 reduction.
Manually querying year by year
Running 10 separate API calls just to compare five countries over ten years.
Use get_generation_multi_entity with a comma-separated list of country codes. It makes massive comparisons efficient.
When It Fits, When It Doesn't
Use this MCP if your analysis requires deep, comparative data across multiple dimensions (Source, Country, Time Period). Specifically, you need to compare monthly usage against yearly trends, or model changes in carbon intensity over time. Don't use it if all you need is a simple list of country names; the get_api_options tool handles that discovery step first. You should avoid using this MCP if your goal is only to check one single metric for one country in one year—those simpler needs might be met elsewhere, but here we prioritize depth and comparison above all else.
Questions you might have
How do I get an Ember Climate API key and how long does it take? +
Simply visit the Ember Climate API page, enter your email address, and click to request your key. You'll receive it via email almost instantly. It only takes 30 seconds — no OAuth apps to configure, no developer portals to navigate, no complex setup.
What countries and regions are covered by the Ember electricity dataset? +
The dataset covers over 200 countries and geographical regions worldwide, including individual nations, continents (like Europe), and regional aggregates (like OECD, EU-27). You can use the get_api_options tool to discover all available entity codes and country names before querying specific data.
Can I compare electricity generation across multiple countries in a single query? +
Yes! Use the get_generation_multi_entity tool and provide comma-separated ISO country codes in the entity_code parameter (e.g., "BRA,DE,US,CHN" for Brazil, Germany, USA, and China). This is highly efficient for comparative energy analysis without making multiple separate API calls.
What energy sources can I filter by when querying electricity generation? +
You can filter by all major energy sources including fossil fuels (coal, gas, oil), renewables (wind, solar, hydro, bioenergy, geothermal), nuclear, and storage. Use the series parameter with values like "coal", "wind", "solar", "hydro", "nuclear", "gas". Call get_api_options with filter_name="series" to see the complete list of available energy types for any dataset.
How do I use the `get_api_options` tool to discover valid filter parameters for electricity datasets? +
The get_api_options tool lists all available entities, energy sources, and date ranges before you write a specific query. This is essential for discovering valid country codes or finding out if a particular time resolution, like quarterly data, is supported by the dataset.
What's the difference between using `get_installed_capacity_monthly` and `get_electricity_generation_monthly`? +
Generation measures how much power was actually produced in a given time period (TWh). Installed capacity tracks the total potential size of infrastructure, such as wind or solar farms. You use capacity data to model future growth potential.
If I want to analyze long-term trends versus seasonal variations, should I prioritize `get_carbon_intensity_yearly` or `get_carbon_intensity_monthly`? +
Use yearly functions for broad, multi-decade comparisons and identifying overall policy shifts. Use monthly functions when you need to pinpoint seasonality, such as tracking peak emissions during a specific wet season or winter heating period.
When running `get_power_sector_emissions_monthly`, how do I ensure I only track the CO2 emission type and not others? +
You must use the series parameter to filter by the specific pollutant. By setting the series parameter (e.g., "co2"), you isolate the desired metric, preventing the tool from returning combined or aggregated emissions data.
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