GA4 BigQuery Semantic Layer

This project provides a semantic layer on top of Google Analytics 4 (GA4) event data stored in Google BigQuery. It uses the boring-semantic-layer library to define a semantic model and exposes it through a Model-View-Controller Protocol (MCP) server.

This allows for consistent and simplified querying of your GA4 data from compatible client applications (like Cursor).

Features

• Connects to your Google BigQuery project and dataset.
• Defines a semantic model for GA4 event data (ga4_events_sm).
• Exposes dimensions like event_date and user_pseudo_id.
• Exposes measures like event_count.
• Runs an MCP server to serve the semantic model, making it available for querying.

Prerequisites

• Python 3.11 or newer.
• Access to a Google Cloud project with the BigQuery API enabled.
• GA4 event data exported to a BigQuery table.
• Google Cloud SDK installed and authenticated on your local machine. You can authenticate by running:

    gcloud auth application-default login

Setup & Installation

1. Clone the repository:

    git clone <your-repository-url>
    cd measurecamp-london-bigqery-mcp

1. Install dependencies:

The project uses uv to manage and run the Python environment. The required dependencies are listed at the top of the layer.py file. uv will install them automatically when you run the server. If you don't have uv, you can install it with:

    pip install uv

Configuration

Before running the server, you need to configure it to point to your BigQuery data. Open the layer.py file and modify the following lines:

1. Update BigQuery Connection:

Change project_id and dataset_id to match your Google Cloud setup.

    con = ibis.bigquery.connect(
        project_id="your-gcp-project-id",
        dataset_id="your_bigquery_dataset_id",
    )

1. Update Table Name:

Change the table name to your GA4 events table.

    ga4_table = con.table("events_YYYYMMDD")

1. Update Primary Key:

The current primary_key in the ga4_events_sm model is set to "code", which is likely a remnant from an example. You should update this to a unique key for your events table or remove it if one is not applicable. A combination of user_pseudo_id and event_timestamp is often used to uniquely identify an event, but boring-semantic-layer currently supports single-column primary keys. For now, you can remove the line.

Running the Server

Once configured, you can start the MCP server by running the following command in your terminal:

uv run layer.py

The server will start and listen for connections from MCP clients.

Usage with an MCP Client (e.g., Cursor)

To connect to this server from an MCP-compatible editor like Cursor, you need to configure it as an MCP server.

1. In Cursor, create or open the .cursor/mcp.json file in your project's root directory.
2. Add the following configuration to the mcpServers object:

    {
        "mcpServers": {
            "ga4-semantic-layer": {
                "command": "uv run layer.py",
                "language": "python"
            }
        }
    }

1. Reload Cursor. You can now use @ga4-semantic-layer in the chat to query your semantic model. For example:

@ga4-semantic-layer How many events were there per day?

This will query your BigQuery table through the semantic layer and return the results.