# Ember Climate MCP MCP

> 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.

## Overview
- **Category:** the-unthinkable
- **Price:** Free
- **Tags:** climate-data, carbon-emissions, electricity-grid, energy-transition, environmental-data, sustainability

## Description

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.

## Tools

### 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.

### get_electricity_demand_yearly
Provides yearly electricity demand data, helping compare per-capita energy use across different nations over time.

### get_electricity_generation_monthly
Returns monthly electricity generation amounts and percentage shares, broken down by specific energy source like coal or wind.

### 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 solar in specific nations.

### get_api_options
Checks the API options to find valid country codes, energy source types, and available date ranges before running a query.

### get_power_sector_emissions_monthly
Retrieves monthly CO2 emissions from the power sector, allowing granular tracking of pollution trends and seasonality.

### get_power_sector_emissions_yearly
Tracks yearly national decarbonization progress by reporting total CO2 emissions from the power sector for a given year.

## Prompt Examples

**Prompt:** 
```
What is the carbon intensity of Brazil's electricity grid in recent years?
```

**Response:** 
```
Fetching carbon intensity data for Brazil... The Brazilian grid shows an intensity of approximately 85 gCO2/kWh in 2023, which is significantly lower than the global average. This is due to Brazil's heavy reliance on hydropower and growing wind/solar capacity.
```

**Prompt:** 
```
Compare wind and solar generation between Germany, China, and the US for the last 3 years.
```

**Response:** 
```
Running multi-entity comparison I've fetched wind and solar generation data for DEU, CHN, and USA from 2021 to 2023. China leads with over 1,000 TWh of combined wind+solar in 2023, followed by the US at ~600 TWh and Germany at ~350 TWh. All three show strong double-digit growth rates year over year.
```

**Prompt:** 
```
Show me the monthly electricity demand in France during 2024.
```

**Response:** 
```
Fetching monthly demand data for France in 2024... Peak demand occurred in January and December (winter heating season) at around 55-60 TWh per month, while summer months like July and August dropped to approximately 35-40 TWh, reflecting strong seasonal variation.
```

## Capabilities

### Analyze Carbon Footprint
Determine the carbon intensity and total CO2 emissions for national electricity grids.

### Track Energy Mix Changes
Break down how much power different sources, like wind or gas, contribute to a country's total generation output.

### Model Demand Trends
Analyze electricity consumption patterns and predict peak load periods across different nations over time.

### Compare Multiple Nations
Query several countries simultaneously to run comparative reports on any metric, like renewable adoption rates.

### Monitor Infrastructure Growth
Track the installation and capacity of clean energy sources like solar and wind power monthly.

## Use Cases

### 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.

## Benefits

- 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.

## How It Works

The bottom line is that your AI agent handles all the complex API calls; you just write the question.

1. Subscribe to this MCP on Vinkius, then input your free Ember Climate API Key.
2. Your agent uses a simple filter discovery tool to check for available countries, sources, and date ranges.
3. 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').

## Frequently Asked Questions

**How do I get an Ember Climate API key and how long does it take?**
Simply visit the [Ember Climate API page](https://ember-energy.org/data/api/), 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.