mcp-server-bigquery

LucasHild/mcp-server-bigquery

🔗 Latest commit:d8c3ad9

🕒 Updated:Sep 9, 2025, 01:05 PM

Python

AI Tools

A Model Context Protocol server that provides access to BigQuery

MCP Trust Score

Based on our comprehensive evaluation criteria

🤖 Evaluated by gemini-2.5-flashFix

Trust Score58/100

• Basic MCP protocol features implemented (12/40)
• GitHub community is not mature yet (12/20)
• Optimal dependency management (20/20)
• Full deployment maturity (10/10)
• Documentation (8/8)
• Archestra MCP Trust score badge is missing

GitHub Metrics

Repository statistics and activity

⭐ GitHub Stars:118

👥 Contributors:5

📋 Total Issues:9

📦 Has Releases:Yes

🔧 Has CI/CD Pipeline:Yes

Configuration

Configuration example extracted from README.md for Claude Desktop and other clients.

🤖 Evaluated by gemini-2.5-flashFix

{
  "mcp-server-bigquery-development": {
    "command": "uv",
    "args": [
      "--directory",
      "{{PATH_TO_REPO}}",
      "run",
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ],
    "env": {}
  },
  "mcp-server-bigquery-published": {
    "command": "uvx",
    "args": [
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ],
    "env": {}
  },
  "mcp-server-bigquery-inspector": {
    "command": "npx",
    "args": [
      "@modelcontextprotocol/inspector",
      "uv",
      "--directory",
      "{{PATH_TO_REPO}}",
      "run",
      "mcp-server-bigquery"
    ],
    "env": {}
  }
}

MCP Protocol Support

Implemented MCP protocol features

🤖 Evaluated by gemini-2.5-flashFix

Tools:✓

Prompts:✗

Resources:✗

Sampling:✗

Roots:✗

Logging:✗

STDIO Transport:✓

HTTP Transport:✗

OAuth2 Auth:✗

Dependencies

2 dependencies

Libraries and frameworks used by this MCP server

🤖 Evaluated by gemini-2.5-flashFix

Main

mcp

google-cloud-bigquery

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 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.

Quickstart

Install

Installing via Smithery

To install BigQuery Server for Claude Desktop automatically via Smithery:

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

Claude Desktop

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

Development/Unpublished Servers Configuration

"mcpServers": {
  "bigquery": {
    "command": "uv",
    "args": [
      "--directory",
      "{{PATH_TO_REPO}}",
      "run",
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}

Published Servers Configuration

"mcpServers": {
  "bigquery": {
    "command": "uvx",
    "args": [
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}

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

Development

Building and Publishing

To prepare the package for distribution:

Increase the version number in pyproject.toml
Sync dependencies and update lockfile:

uv sync

Build package distributions:

uv build

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

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.

Resources

GitHub Repository

Add Quality Badge

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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 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.

Quickstart

Install

Installing via Smithery

To install BigQuery Server for Claude Desktop automatically via Smithery:

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

Claude Desktop

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

Development/Unpublished Servers Configuration

"mcpServers": {
  "bigquery": {
    "command": "uv",
    "args": [
      "--directory",
      "{{PATH_TO_REPO}}",
      "run",
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}

Published Servers Configuration

"mcpServers": {
  "bigquery": {
    "command": "uvx",
    "args": [
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}

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

Development

Building and Publishing

To prepare the package for distribution:

Increase the version number in pyproject.toml
Sync dependencies and update lockfile:

uv sync

Build package distributions:

uv build

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

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.