Data Intelligence

This project builds a BigQuery-powered data assistant that combines an MCP server with a Streamlit chat interface. The backend exposes BigQuery operations through MCP tools, while the frontend lets users ask questions in natural language and receive answers with the help of an agent.

What this project does

• Exposes BigQuery tools from the MCP server in main.py
• Lists tables and fetches table schemas
• Generates and executes BigQuery queries
• Provides a chat-based UI in app.py for interacting with your data

Project structure

• main.py: Defines the FastMCP server and BigQuery tool wrappers
• app.py: Streamlit UI that connects to the MCP server and runs the agent
• pyproject.toml: Project dependencies and Python metadata

Prerequisites

Before running the project, make sure you have:

• Python 3.13 or newer
• A Google Cloud project with BigQuery access
• A valid Google Cloud credential configuration for BigQuery
• An Anthropic API key for the chat model used in the app

Setup

1. Create and activate a virtual environment.
2. Install the dependencies:
   • pip install -r requirements.txt
   • or uv sync
3. Create a .env file in the project root with the required environment variables:

ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_APPLICATION_CREDENTIALS=path/to/your/credentials.json

4. Update the placeholder values in main.py for your BigQuery project and dataset.

Running the app

Start the MCP server first:

python main.py

In a second terminal, start the Streamlit app:

streamlit run app.py

The app expects the MCP server to be available at:

http://localhost:8000/mcp

Notes

• The current implementation in main.py uses placeholder values such as project_id and dataset_name. Replace these with your actual BigQuery project and dataset before running the app.
• The UI is designed for natural language interaction with BigQuery data, but the underlying SQL execution depends on your configured BigQuery access and credentials.