What data does it use?
It can analyze a sample `transactions.csv` file or connect to real Bunq accounts via the Bunq API key stored in `.env`.
@Rohit8y
Basic information
AI Agents to support sustainable spending on Bunq using MCP servers
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"bunq-hackathon-agents-6.0": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tool list not available yet
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A financial analytics tool that connects your spending data to climate impact. It uses LLMs to classify transactions, calculate carbon footprint, and enable automated carbon offset transfers via Bunq. Built for individuals who want to understand and reduce their environmental impact through everyday spending.
Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, and add a Bunq API key to a .env file. Then run streamlit run dashboard.py to launch the interactive dashboard.
It can analyze a sample `transactions.csv` file or connect to real Bunq accounts via the Bunq API key stored in `.env`.
Python 3 with Streamlit, `pydantic_ai`, Gemini/OpenAI LLM access, Pandas, Matplotlib, and the Bunq SDK. All are listed in `requirements.txt`.
Green (CO₂ per € < 0.4), Yellow (0.4–0.6), Red (> 0.6). The zone is displayed as a dynamic badge in the app.
Offset transfers require a Bunq account and API key. Other features (classification, CO₂ calculation, visualization) work with the sample data.
The dashboard uses Gemini and OpenAI via the `pydantic_ai` agent orchestration library for spending classification and CO₂ reasoning.

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