@PaddyAlton
Basic information
An MCP server to help AI Agents inspect the contents of a BigQuery warehouse
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"bigquery-mcp": {
"command": "uv",
"args": [
"run",
"--with",
"mcp",
"--directory",
"/path/to/bigquery-mcp",
"mcp",
"run",
"/path/to/bigquery-mcp/src/server.py"
],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": ""
}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
bigquery-mcp is a Model Context Protocol (MCP) server that provides AI agents with tools to examine the contents of a BigQuery data warehouse—including datasets, tables, columns, and query history. It is designed for developers using AI-powered coding tools (e.g., Cursor) to improve the quality of SQL queries by giving the agent context about the database schema and data.
After ensuring uv is installed and a BigQuery client is available (via gcloud or a service account key set in GOOGLE_APPLICATION_CREDENTIALS), clone the repository and run uv sync. Then, in Cursor settings under MCP Servers, start a server with the command: uv run --with mcp --directory /path/to/bigquery-mcp mcp run /path/to/bigquery-mcp/src/server.py. A contextual rule file (.cursor/rules/tool-use-rule.mdc) is recommended to give the Cursor Agent proper instructions for using the tools.
uv for dependency management and BigQuery client library for authentication.You need `uv` installed (e.g., via `brew install uv`) and a BigQuery client that can transparently create a client—usually from having `gcloud` installed locally. Alternatively, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to a service account key.
In Cursor settings, go to MCP Servers and add a new server with the command: `uv run --with mcp --directory /path/to/bigquery-mcp mcp run /path/to/bigquery-mcp/src/server.py`.
The server provides tools to examine BigQuery datasets, tables, columns, and query history, giving the agent the context it needs to write better SQL queries.
For development you also need `Taskfile` installed (e.g., `brew install go-task`). The project uses a Taskfile for task automation.
It currently assumes you can transparently create a BigQuery `Client`. This works when `gcloud` is installed locally—in other environments you can provide a service account key via the `GOOGLE_APPLICATION_CREDENTIALS` environment variable.
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