Server data from the Official MCP Registry
An MCP server for calculating carbon footprints from bank statements using EPA GHG emission factors.
An MCP server for calculating carbon footprints from bank statements using EPA GHG emission factors.
This carbon footprint calculation MCP server is well-structured with appropriate security posture for its stated purpose. It performs local emissions calculations without external API calls or data exfiltration. No authentication is required because all operations are read-only calculations on user-provided data. Minor code quality observations exist around input validation and error handling, but these do not constitute security vulnerabilities given the server's non-sensitive computational nature. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
3 files analyzed · 7 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mayanktalwar0-carbon-footprint-mcp": {
"args": [
"carbon-footprint-mcp"
],
"command": "uvx"
}
}
}From the project's GitHub README.
An MCP (Model Context Protocol) server for calculating organizational carbon footprints from bank statements, financial exports, and structured activity data using EPA GHG emission factors.
Privacy and security first
- Runs 100% locally on your machine or server
- Sends no financial data to external APIs or cloud providers
- Stores no data by default
- Exposes read-only calculation and reporting tools
- Works with Claude Desktop, Cursor, and other MCP clients
If you are preparing ESG reporting, investor diligence materials, or internal sustainability reviews, getting to a usable emissions baseline is usually slow and manual.
This server helps turn raw bank statements, Xero or QBO exports, and structured operational inputs into a carbon footprint report in minutes. It maps activities to EPA-aligned emission factors and produces both HTML and Markdown outputs.
The user experience is designed to work for organizations in any country, while the current electricity benchmarking still uses EPA eGRID regional factors under the hood.
All emission factors are based on the EPA GHG Emission Factors Hub (January 2025), including eGRID 2023 electricity factors and IPCC AR5 global warming potentials.
Covered categories include stationary combustion, mobile combustion, electricity, steam or heat, transportation, waste disposal, business travel, employee commuting, and refrigerants.
uv.{
"mcpServers": {
"carbon-footprint": {
"command": "uvx",
"args": ["carbon-footprint-mcp"]
}
}
}
claude mcp add carbon-footprint -- uvx carbon-footprint-mcp
git clone https://github.com/MayankTalwar0/carbon-footprint-mcp.git
cd carbon-footprint-mcp
pip install -e .
carbon-footprint-mcp
| Tool | Description |
|---|---|
computeEmissions(inputs_json) | Computes GHG emissions from structured activity data across all 3 scopes. |
generateEmissionsReport(emissions_json, output_dir) | Renders a polished HTML and Markdown report and saves it to disk. |
listEmissionFactors(category) | Lists available fuel, eGRID, and waste emission factors. |
| Scope | Category | Input Required |
|---|---|---|
| 1 | Stationary Combustion | Fuel type and quantity |
| 1 | Mobile Combustion | Fuel type and gallons |
| 1 | Refrigerant Leakage | Gas type, leaked kg, and GWP |
| 2 | Purchased Electricity | kWh and eGRID subregion |
| 2 | Purchased Steam or Heat | mmBtu |
| 3 | Transportation and Distribution | Vehicle type and distance |
| 3 | Waste Disposal | Material, short tons, and disposal method |
| 3 | Business Travel | Travel mode and passenger-miles |
| 3 | Employee Commuting | Commute mode and passenger-miles |
| Score | tCO2e per $1M Revenue | Interpretation |
|---|---|---|
| Excellent | < 5 | Best-in-class for low-footprint operations |
| Good | 5-20 | Low intensity |
| Moderate | 20-100 | Typical for services and tech |
| High | 100-500 | Heavy operations |
| Very High | > 500 | Very high intensity |
MIT
Built by Mayank, founder of SlickBooks.
Be the first to review this server!
by Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
by mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.