Ember Climate MCP. Analyze global energy mix, demand, and emissions data.
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Ember Climate provides instant access to global electricity data. Query generation mix, demand trends, and emissions data across over 200 countries.
Analyze carbon intensity (gCO2/kWh) and track renewable capacity deployment monthly or yearly using specific tools. Perfect for energy consultants, researchers, and policymakers needing authoritative, time-series grid data.
What your AI agents can do
Get api options
Checks available filters, energy sources, and date ranges for all Ember electricity datasets.
Get carbon intensity monthly
Gets the monthly carbon intensity (gCO2/kWh) for specified countries and date ranges.
Get carbon intensity yearly
Gets the yearly carbon intensity (gCO2/kWh) for specified countries and date ranges.
Determine a country's grid carbon footprint (gCO2/kWh) over specified time periods, tracking seasonal changes or long-term decarbonization.
Pull monthly or yearly electricity consumption data (TWh) for specific countries, helping identify peak usage periods and growth trends.
Retrieve total electricity generation (TWh) and the percentage share for specific sources like coal, wind, or solar, both monthly and yearly.
Get monthly or yearly CO2 emissions data (megatonnes) for the power sector, useful for tracking climate policy impact.
Run complex comparative analyses across several countries simultaneously without running individual queries.
List available filters, energy sources, and date ranges before running a specific data query.
Ask AI about this MCP
Supported MCP Clients
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Ember Climate MCP Server: 11 Tools for Global Energy Analysis
These 11 tools let your AI client query everything from carbon intensity and electricity demand to generation mix and installed capacity across any number of global regions.
019d758fget api options
Checks available filters, energy sources, and date ranges for all Ember electricity datasets.
019d758fget carbon intensity monthly
Gets the monthly carbon intensity (gCO2/kWh) for specified countries and date ranges.
019d758fget carbon intensity yearly
Gets the yearly carbon intensity (gCO2/kWh) for specified countries and date ranges.
019d758fget electricity demand monthly
Gets the monthly electricity demand (TWh) for specified countries and date ranges.
019d758fget electricity demand yearly
Gets the yearly electricity demand (TWh) for specified countries and date ranges.
019d758fget electricity generation monthly
Gets monthly electricity generation (TWh) and source shares for specified countries and dates.
019d758fget electricity generation yearly
Gets yearly electricity generation (TWh) and source shares for specified countries and dates.
019d758fget generation multi entity
Gets electricity generation data for multiple countries in a single call.
019d758fget installed capacity monthly
Gets the monthly installed power capacity (GW) for wind and solar in specified countries.
019d758fget power sector emissions monthly
Gets monthly power sector CO2 emissions (megatonnes) for specified countries and dates.
019d758fget power sector emissions yearly
Gets yearly power sector CO2 emissions (megatonnes) for specified countries and dates.
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What you can do with this MCP connector
Ember Climate gives your AI client direct access to global electricity data for over 200 countries. You can query generation mix, demand trends, and emissions data across the globe.
To analyze a grid's carbon footprint, you'll get the monthly or yearly carbon intensity (gCO2/kWh) for any country you name. You can also pull monthly or yearly electricity demand (TWh) for specific nations, which helps you track usage trends and peak periods. To map out how electricity is generated, you can retrieve the total generation (TWh) and the percentage share for sources like coal, wind, or solar, both monthly and yearly.
You'll get monthly or yearly CO2 emissions data (megatonnes) for the entire power sector. You can track clean energy deployment by getting the monthly installed power capacity (GW) for wind and solar. For complex comparisons, you can run multiple analyses across several countries in a single call. You can check available filters, energy sources, and date ranges for all datasets using get_api_options.
You can get monthly electricity generation (TWh) and source shares, or yearly electricity generation (TWh) and source shares, and you can get electricity demand data for multiple countries at once using get_generation_multi_entity.
How Ember Climate MCP Works
- 1 Subscribe to the Ember Climate server and enter your free API key.
- 2 Your AI client sends a request, specifying the data needed (e.g., 'monthly carbon intensity').
- 3 The server runs the query, returning structured data on global energy metrics for the specified countries and time ranges.
The bottom line is your AI client handles the complex API calls and data formatting, giving you clean, actionable energy statistics directly in the chat.
Who Is Ember Climate MCP For?
Climate researchers who need to map global emissions trends. Energy consultants comparing generation mixes across continents. Sustainability teams tracking corporate ESG goals against national standards. Policymakers and journalists fact-checking energy claims with authoritative, real-time data.
Retrieves historical emissions data and grid carbon intensity trends without building data pipelines.
Compares generation mixes (e.g., US vs. DE) and tracks renewable adoption rates programmatically.
Monitors power sector decarbonization progress and contextualizes corporate ESG targets against national benchmarks.
Fact-checks energy claims and analyzes energy consumption patterns for legislative proposals.
What Changes When You Connect
- Track decarbonization progress by comparing monthly carbon intensity using
get_carbon_intensity_monthly. You see if a grid is getting cleaner quarter over quarter. - Compare multiple nations instantly. Use
get_generation_multi_entityto run a single query comparing, say, US, China, and Germany’s combined wind and solar output. - Analyze energy use over time. Use
get_electricity_demand_yearlyto see long-term per-capita consumption trends across different countries. - Understand infrastructure build-out. Use
get_installed_capacity_monthlyto monitor monthly wind and solar capacity growth in any country. - Get a full picture of the energy source.
get_electricity_generation_monthlybreaks down total TWh by coal, gas, and renewables for detailed mix analysis. - Avoid repetitive calls. Use
get_api_optionsfirst. This lets you discover valid country codes or date formats before writing a single query.
Real-World Use Cases
Comparing BRICS+ Energy Mixes
An energy consultant needs to compare the generation mixes of Brazil, India, and China. They use get_generation_multi_entity with the combined country codes. This single call provides TWh and percentage shares for all sources, saving them from running three separate queries and stitching the results together.
Modeling Seasonal Demand Peaks
A utilities planner wants to know the difference between peak winter usage and summer usage for France. They use get_electricity_demand_monthly for 2024. The agent returns data showing peak demand in January/December (55-60 TWh), and lower summer demand (35-40 TWh), highlighting the strong seasonal variation.
Tracking Renewable Deployment Goals
A sustainability team monitors the EU's renewable targets. They use get_installed_capacity_monthly to follow monthly wind and solar capacity growth (GW) across several member states over the last three years, verifying if deployment goals are on track.
Fact-Checking Historical Emissions Claims
A journalist needs to verify a claim about China’s emissions reduction over the last decade. They run get_power_sector_emissions_yearly for China from 2013 to 2023. The agent provides the annual CO2 emissions (megatonnes), allowing them to fact-check the claim with raw, authoritative data.
The Tradeoffs
Querying every dataset separately
Calling get_electricity_generation_yearly, then get_electricity_demand_yearly, then get_power_sector_emissions_yearly for the same country and time period. This requires multiple API calls and a lot of manual comparison.
→
Define the scope (e.g., 'US, 2020-2023'). Then, use the most efficient tools like get_generation_multi_entity for generation, followed by get_power_sector_emissions_yearly for emissions, minimizing calls and streamlining the data flow.
Forgetting multi-country queries
Running separate queries for every country (e.g., one for 'Brazil', one for 'DE', one for 'US') when comparing them. This is slow and highly redundant.
→
Use get_generation_multi_entity. Simply pass comma-separated country codes (e.g., 'BRA,DE,US') to get comparable data for all nations in one shot.
Assuming data granularity
Trying to compare a monthly capacity report (get_installed_capacity_monthly) directly to a yearly emissions report (get_power_sector_emissions_yearly) without checking the date formats. The data won't align.
→
Always use get_api_options first. This tells you the exact date formats and time resolutions available for each specific dataset before you write your main query.
When It Fits, When It Doesn't
Use this server if your goal is deep, multi-dimensional analysis of global energy systems. You need to compare metrics like generation mix, demand, and carbon intensity across multiple countries and time periods.
Don't use it if you just need a simple, single-point fact check (e.g., 'What is the total demand in France in 2023?'). For that, a simpler, single-purpose query tool might suffice. However, if that simple query is part of a larger analysis, this server is still better because it provides the necessary structure to build out a complete picture.
Remember: get_generation_multi_entity is your best friend for comparative work. If you are focused purely on one country's long-term emissions, get_power_sector_emissions_yearly is the most direct tool.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ember Climate. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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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 server provides 11 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Figuring out a country's energy mix used to mean hours of data wrangling.
Before this server, analyzing energy data meant downloading massive CSV files from government sites. You'd spend half a day cleaning up headers, merging demand data with generation mix, and then writing Python scripts just to calculate the carbon intensity metric. It was slow, brittle, and required a full-time data analyst.
Now, your agent handles it. You ask, 'What was the carbon intensity of the US grid in 2022?' and the agent uses `get_carbon_intensity_yearly`. You get the number instantly, formatted and ready to use in your report. No spreadsheets, no manual clean-up.
Ember Climate MCP Server: Get the full picture with `get_generation_multi_entity`
Comparing multiple nations' energy data used to mean running a separate query for every single country. If you wanted to compare the top 5 emitters, you were hitting the API 5 times, waiting 5 times, and then pasting 5 sets of results into a spreadsheet for comparison.
Now, you simply list the country codes (e.g., 'BRA,DE,US,CHN') and run `get_generation_multi_entity`. The server returns one unified dataset, allowing you to see how multiple grids compare side-by-side in a single view.
Common Questions About Ember Climate MCP
How do I know what countries or energy sources are available? (get_api_options) +
Use get_api_options first. It runs a quick check and lists all available filters, energy source types, and valid country codes before you build your main query. This prevents errors down the line.
Can I compare multiple countries' generation mix in one go? (get_generation_multi_entity) +
Yes, use get_generation_multi_entity. You just pass a comma-separated list of country codes (e.g., 'BRA,DE,US') to compare generation data across many nations simultaneously.
What is the difference between monthly and yearly data? (get_electricity_demand_monthly vs get_electricity_demand_yearly) +
Monthly data (get_electricity_demand_monthly) shows seasonal variations, like high demand in winter. Yearly data (get_electricity_demand_yearly) provides the long-term consumption trend, smoothing out the seasonal peaks.
How do I track clean energy growth over time? (get_installed_capacity_monthly) +
Use get_installed_capacity_monthly. This tool tracks the actual capacity additions (GW) for wind and solar, showing how rapidly a country's clean energy infrastructure is growing month by month.
Do I need a separate tool for emissions? (get_power_sector_emissions_yearly vs get_power_sector_emissions_monthly) +
Choose based on your goal. Use yearly data for high-level policy comparisons. Use monthly data for granular tracking of seasonal pollution spikes.
How do I analyze seasonal carbon intensity using `get_carbon_intensity_monthly`? +
You use the get_carbon_intensity_monthly tool. Simply provide the country code and a date range in YYYY-MM format. This lets you see if carbon emissions spike during specific seasons, like winter heating, or steadily decline year over year.
Can I track generation mix changes for multiple sources using `get_electricity_generation_yearly`? +
Yes, you specify the sources using the series parameter. You just need to list the energy types you want—like 'coal', 'wind', and 'solar'—and provide the country code and date range. The tool then breaks down the total generation by each source for you.
Is there a limit to how many countries I can compare at once with `get_generation_multi_entity`? +
The tool accepts a comma-separated list of country codes for comparison. While the exact limit isn't listed, you can typically compare a dozen or more nations efficiently in a single call. Always check the API documentation for the maximum entity count.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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