BigQuery MCP server

GitHub
  • Databases
Python
MIT
8
  • Apple
-
security - not tested
A
license - permissive license (MIT)
-
quality - not tested

A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.

  1. Tools
  2. Prompts
  3. Resources
  4. Server Configuration
  5. README.md

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Tools

Functions exposed to the LLM to take actions

NameDescription

No tools

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
GCP_LOCATIONYesThe GCP location (e.g. europe-west9).
GCP_PROJECT_IDYesThe GCP project ID.
UV_PUBLISH_TOKENNoToken for publishing to PyPI. Can also be set via command flag --token.
UV_PUBLISH_PASSWORDNoPassword for publishing to PyPI. Can also be set via command flag --password.
UV_PUBLISH_USERNAMENoUsername for publishing to PyPI. Can also be set via command flag --username.
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README.md

BigQuery MCP server

A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.

Components

Tools

The server implements one tool:

  • execute-query: Executes a SQL query using BigQuery dialect
  • list-tables: Lists all tables in the BigQuery database
  • describe-table: Describes the schema of a specific table

Configuration

The server can be configured with the following arguments:

  • --project (required): The GCP project ID.
  • --location (required): The GCP location (e.g. europe-west9).
  • --dataset (optional): Only take specific BigQuery datasets into consideration. Several datasets can be specified by repeating the argument (e.g. --dataset my_dataset_1 --dataset my_dataset_2). If not provided, all tables in the project will be considered.

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

<details> <summary>Development/Unpublished Servers Configuration</summary> ``` "mcpServers": { "bigquery": { "command": "uv", "args": [ "--directory", "{{PATH_TO_REPO}}", "run", "mcp-server-bigquery", "--project", "{{GCP_PROJECT_ID}}", "--location", "{{GCP_LOCATION}}" ] } } ``` </details> <details> <summary>Published Servers Configuration</summary> ``` "mcpServers": { "bigquery": { "command": "uvx", "args": [ "mcp-server-bigquery", "--project", "{{GCP_PROJECT_ID}}", "--location", "{{GCP_LOCATION}}" ] } } ``` </details>

Replace {{PATH_TO_REPO}}, {{GCP_PROJECT_ID}}, and {{GCP_LOCATION}} with the appropriate values.

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:
uv sync
  1. Build package distributions:
uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:
uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN
  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory {{PATH_TO_REPO}} run mcp-server-bigquery

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues with dependencies of the server.
  • Extract server characteristics such as tools, resources, prompts, and required parameters.

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