Skip to main content

BigQuery MCP server

smithery badge

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 either with command line arguments or environment variables.

Argument Environment Variable Required Description
--project BIGQUERY_PROJECT Yes The GCP project ID.
--location BIGQUERY_LOCATION Yes The GCP location (e.g. europe-west9).
--dataset BIGQUERY_DATASETS No 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) or by joining them with a comma in the environment variable (e.g. BIGQUERY_DATASETS=my_dataset_1,my_dataset_2). If not provided, all datasets in the project will be considered.
--key-file BIGQUERY_KEY_FILE No Path to a service account key file for BigQuery. If not provided, the server will use the default credentials.
--timeout BIGQUERY_TIMEOUT No Maximum time in seconds to wait for a query to complete. If not provided, queries will wait indefinitely.

Installation

Installing via Smithery

To install BigQuery Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp-server-bigquery --client claude

Claude Code

claude mcp add bigquery --scope user --transport stdio -- uvx mcp-server-bigquery --project {PROJECT_ID} --location {{LOCATION}}

Claude Desktop

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

{
  "mcpServers": {
    "bigquery": {
      "command": "uvx",
      "args": ["mcp-server-bigquery"],
      "env": {
        "BIGQUERY_PROJECT": "{{GCP_PROJECT_ID}}",
        "BIGQUERY_LOCATION": "{{GCP_LOCATION}}"
      }
    }
  }
}

Cursor

  1. Open Cursor Settings → MCP
  2. Click Add new global MCP server
  3. Add an entry for the BigQuery MCP, following the pattern below:
{
  "mcpServers": {
    "bigquery": {
      "command": "uvx",
      "args": ["mcp-server-bigquery"],
      "env": {
        "BIGQUERY_PROJECT": "{{GCP_PROJECT_ID}}",
        "BIGQUERY_LOCATION": "{{GCP_LOCATION}}"
      }
    }
  }
}

Metadata

Release files for mcp-server-bigquery 0.3.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mcp-server-bigquery 0.3.2
File Size Uploaded Provenance
mcp_server_bigquery-0.3.2.tar.gz 20.7 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for mcp-server-bigquery 0.3.2
File Interpreter ABI Platform
mcp_server_bigquery-0.3.2-py3-none-any.whl Python 3 none any Details

Total release size: 27.2 kB

Release files / mcp_server_bigquery-0.3.2.tar.gz

Download URL mcp_server_bigquery-0.3.2.tar.gz
Size 20.7 kB
Tags Source
SHA-256 checksum
How to use checksums
844b8ad2e8121ce509e9dfe32e53a47212d0f86fb2dc455e39260ff2719f261d
BLAKE2b-256 checksum
How to use checksums
9b71023112a409d1f9fd21a5106f847097994c656d18c67efb992b75f8211053
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.6.9

Release files / mcp_server_bigquery-0.3.2-py3-none-any.whl

Download URL mcp_server_bigquery-0.3.2-py3-none-any.whl
Size 6.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
eff10473671a5c12f89a60f744387b4aa06a7f4aead919fbb844b3c7aa9bfaaf
BLAKE2b-256 checksum
How to use checksums
6a901d72fe65d727a8eaf1a6389edbc20ca3341fa89edec2617abe94ebdea97b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.6.9

Security for mcp-server-bigquery 0.3.2

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page