Skip the Carbon Calculation Headaches with AI-Powered Emissions Data

Building sustainability features shouldn't require becoming a carbon accounting expert. This MCP server puts Climatiq's comprehensive emissions database directly into your AI assistant's toolkit, turning complex carbon calculations into simple conversations.

Stop Wrestling with Emission Factor Spreadsheets

You know the drill: your product needs carbon footprint calculations, so you dive into emission factor databases, try to figure out regional variations, and end up with a mess of hardcoded values that go stale. Meanwhile, your AI assistant sits there, perfectly capable of handling the complexity—if only it had access to real emissions data.

This MCP server changes that. Instead of building carbon calculation infrastructure from scratch, you get instant access to Climatiq's professional-grade emission factors through your existing AI workflows.

Real Carbon Intelligence, Zero Learning Curve

Point your AI assistant at actual emission calculations without the typical integration overhead:

# Instead of this mess:
emission_factor = lookup_electricity_factor("US", "2023", "residual_mix")
carbon_kg = energy_kwh * emission_factor * grid_loss_factor
# ...plus regional adjustments, data validation, error handling

# You get this:
"Calculate emissions for 1,000 kWh of electricity usage in California"
# AI assistant handles the complexity, returns accurate results

The server exposes 9 specialized tools covering the emission calculations you actually need:

• Electricity consumption by region and grid mix
• Travel emissions for cars, planes, trains with distance or spend-based methods
• Cloud computing resource usage
• Freight transportation across different modes
• Procurement and hotel spend-based calculations
• Custom calculations using specific emission factors

Skip the Carbon API Learning Curve

Climatiq's API is powerful but requires understanding activity IDs, regional variations, and calculation methodologies. This MCP server handles that complexity, letting you focus on building features instead of becoming a carbon data expert.

# Direct API requires this knowledge:
curl -X POST "https://api.climatiq.io/v1/estimates" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "emission_factor": {
      "activity_id": "passenger_vehicle-vehicle_type_car-fuel_source_na",
      "region": "US",
      "year": 2022
    },
    "parameters": {
      "distance": 100,
      "distance_unit": "km"
    }
  }'

# MCP server lets your AI handle it:
"What are the emissions from driving 100km in a car in the US?"

Real-World Integration Scenarios

Building a corporate carbon tracker? Your AI assistant can calculate emissions from employee travel, office electricity usage, and cloud infrastructure with natural language queries.

Adding sustainability features to an e-commerce platform? Get shipping emission calculations for different carrier options, helping customers make informed choices.

Developing a carbon budgeting tool? Access thousands of emission factors for procurement categories, from office supplies to manufacturing equipment.

Creating travel planning features? Compare emission impacts across different transportation modes with real-time calculations.

Install and Connect in Minutes

# Install via uv
uv pip install climatiq-mcp-server

# Set your Climatiq API key
export CLIMATIQ_API_KEY=your_api_key

# Start the server
climatiq-mcp-server

Add to your Claude Desktop config:

{
  "mcpServers": {
    "climatiq-mcp-server": {
      "command": "climatiq-mcp-server",
      "env": {
        "CLIMATIQ_API_KEY": "your_api_key"
      }
    }
  }
}

Beyond Basic Calculations

This isn't just a simple API wrapper. The server includes:

• Resource caching to preserve calculation history
• Natural language explanations for carbon impact context
• Multiple calculation methods for different data availability scenarios
• Comprehensive emission factor search across Climatiq's database
• CLI tools for testing and development workflows

The included utilities let you test calculations, explore the API, and even run calculations outside the MCP context when needed.

Production-Ready Architecture

Built with FastAPI and proper error handling, this server is ready for real applications. The codebase includes comprehensive examples, from simple API tests to full Jupyter notebooks demonstrating advanced usage patterns.

The developer put serious thought into making this maintainable—clear separation between MCP protocol handling and Climatiq API integration, proper logging, and multiple deployment options.

Get Started Now

Stop building carbon calculation infrastructure from scratch. This MCP server gives your AI assistant access to professional-grade emissions data, letting you build sustainability features that actually work.

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