Which integrations are available for this server?

Enables interaction with Google BigQuery datasets, tables, schemas, and queries, including listing datasets and tables, retrieving schemas, dry-running and executing SQL queries.

How do I use BigQuery MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@BigQuery MCP Server list datasets in my project" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

BigQuery MCP Server

by niduran-orion

Overview Schema Related Servers Score Discussions

Python

Remote

BigQuery MCP Server

A Model Context Protocol (MCP) server that enables LLMs to interact with Google BigQuery. Built with FastMCP (Python) and designed for ephemeral cloud environments where only environment variables are available (no file-based credentials).

Features

Tool	Description
`bq_list_datasets`	List all datasets in the configured project
`bq_list_tables`	List tables in a dataset with row counts and size in MB
`bq_get_schema`	Get the full schema of a table (columns, types, descriptions)
`bq_dry_run`	Validate SQL and estimate query cost (~$6.25/TB)
`bq_run_query`	Execute SQL and return results (auto-appends LIMIT 1000 if missing)

Related MCP server: BigQuery MCP Server

Project Structure

bigquery-mcp-server/
├── src/
│   ├── server.py                 # Entry point — starts the MCP server
│   ├── constants.py              # Global constants (BigQuery config, limits)
│   ├── tools/
│   │   └── bigquery.py           # All 5 BigQuery tools
│   └── services/
│       ├── bigquery_client.py    # BigQuery client factory + row serialization
│       └── error_handler.py      # Centralized BigQuery error handling
├── main.py                       # Root-level entry point wrapper
├── Dockerfile                    # Multi-stage Docker build
├── docker-compose.yml            # Server + MCP Inspector
├── docker-compose.dev.yml        # Dev mode with hot-reload
├── mcp.json                      # MCP client configuration
├── pyproject.toml                # Project metadata and dependencies
└── requirements.txt              # Pip-compatible dependencies

Environment Variables

Variable	Required	Default	Description
`BIGQUERY_PROJECT_ID`	Yes	—	GCP project ID (e.g. `my-project-123`)
`BIGQUERY_SERVICE_ACCOUNT_JSON_CONTENT`	Yes	—	Raw JSON string of the service account key
`BIGQUERY_LOCATION`	No	`US`	BigQuery dataset region
`TRANSPORT`	No	`stdio`	Transport mode: `stdio` or `http`
`PORT`	No	`8000`	HTTP server port
`HOST`	No	`127.0.0.1`	HTTP server bind address

Authentication

This server authenticates using a service account JSON provided directly via environment variable — no file paths, no gcloud CLI.

Create a service account in the GCP Console with at least:
- BigQuery Data Viewer (roles/bigquery.dataViewer)
- BigQuery Job User (roles/bigquery.jobUser)
Generate a JSON key for the service account
Set the full JSON string as BIGQUERY_SERVICE_ACCOUNT_JSON_CONTENT

export BIGQUERY_PROJECT_ID="my-project-123"
export BIGQUERY_SERVICE_ACCOUNT_JSON_CONTENT='{"type":"service_account","project_id":"...","private_key":"...","client_email":"...","...":"..."}'

Quick Start

Option A — uvx (Recommended)

No manual install needed. uvx downloads the package from PyPI, creates an isolated environment, and runs the server:

uvx bigquery-mcp-server

For MCP clients (Claude Code, Cursor, etc.), configure the server in your client's MCP config:

{
  "mcpServers": {
    "bigquery-mcp": {
      "command": "uvx",
      "args": ["bigquery-mcp-server"],
      "env": {
        "BIGQUERY_PROJECT_ID": "my-project-123",
        "BIGQUERY_SERVICE_ACCOUNT_JSON_CONTENT": "{ ... }"
      }
    }
  }
}

Note: Env vars must be passed explicitly via the env block in your MCP client config.

Option B — Local (virtualenv)

# Create virtual environment and install
python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate
pip install -e .

# Set credentials (or use a .env file)
export BIGQUERY_PROJECT_ID="my-project-123"
export BIGQUERY_SERVICE_ACCOUNT_JSON_CONTENT='{ ... }'

# Run in stdio mode
python main.py

# Or HTTP mode (for MCP Inspector)
TRANSPORT=http python main.py

Option C — Docker

# Set env vars in a .env file or export them, then:
docker compose up --build

# Open MCP Inspector at http://localhost:6274
# Connect to: http://mcp-server:8000/mcp (Streamable HTTP)

Option D — Dev mode with hot-reload

docker compose -f docker-compose.dev.yml up --build

# In another terminal:
npx @modelcontextprotocol/inspector
# Connect to: http://localhost:8000/mcp (Streamable HTTP)

Testing with MCP Inspector CLI

# List all tools
npx @modelcontextprotocol/inspector --cli \
  --config mcp.json --server bigquery-mcp \
  --method tools/list

# List datasets
npx @modelcontextprotocol/inspector --cli \
  --config mcp.json --server bigquery-mcp \
  --method tools/call --tool-name bq_list_datasets

# List tables in a dataset
npx @modelcontextprotocol/inspector --cli \
  --config mcp.json --server bigquery-mcp \
  --method tools/call --tool-name bq_list_tables \
  --tool-arg 'params={"dataset": "my_dataset"}'

# Get table schema
npx @modelcontextprotocol/inspector --cli \
  --config mcp.json --server bigquery-mcp \
  --method tools/call --tool-name bq_get_schema \
  --tool-arg 'params={"dataset": "my_dataset", "table": "my_table"}'

# Dry run a query
npx @modelcontextprotocol/inspector --cli \
  --config mcp.json --server bigquery-mcp \
  --method tools/call --tool-name bq_dry_run \
  --tool-arg 'params={"sql": "SELECT * FROM my_dataset.my_table"}'

# Run a query
npx @modelcontextprotocol/inspector --cli \
  --config mcp.json --server bigquery-mcp \
  --method tools/call --tool-name bq_run_query \
  --tool-arg 'params={"sql": "SELECT * FROM my_dataset.my_table LIMIT 10"}'

Connect to Claude Code

Using uvx (recommended):

claude mcp add bigquery-mcp uvx -- bigquery-mcp-server

Or add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "bigquery-mcp": {
      "command": "uvx",
      "args": ["bigquery-mcp-server"],
      "env": {
        "BIGQUERY_PROJECT_ID": "my-project-123",
        "BIGQUERY_SERVICE_ACCOUNT_JSON_CONTENT": "{ ... }"
      }
    }
  }
}

Transports

Transport	How to activate	When to use
stdio	`TRANSPORT=stdio python -m src.server`	Claude Code, Cursor, local integration
HTTP	`TRANSPORT=http python -m src.server`	MCP Inspector, remote servers

Safety Features

Auto LIMIT: bq_run_query automatically appends LIMIT 1000 if no LIMIT clause is detected, preventing accidental large data transfers
Dry run: bq_dry_run validates SQL and estimates cost before execution
Input validation: All tool inputs validated with Pydantic v2
Error handling: BigQuery exceptions (403, 404, 400, etc.) are caught and returned as human-readable strings — the server never crashes
Type serialization: Dates, datetimes, decimals, and bytes are automatically converted to JSON-safe types

References

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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