Skip to main content

Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery

Project description

BigQuery MCP Server

Python Version Framework

This is a Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery, caches it locally, and serves it via MCP. Its primary purpose is to enable generative AI systems to quickly understand BigQuery's structure and execute queries securely.

Key Features

  • Metadata Management: Retrieves and caches information about BigQuery datasets, tables, and columns
  • Keyword Search: Supports keyword search of cached metadata
  • Secure Query Execution: Provides SQL execution capabilities with automatic LIMIT clause insertion and cost control
  • MCP Compliance: Offers tools via the Model Context Protocol

MCP Server Tools

Available tools:

  1. get_datasets - Retrieves a list of all datasets
  2. get_tables - Retrieves all tables within a specified dataset (requires dataset_id, optionally accepts project_id)
  3. search_metadata - Searches metadata for datasets, tables, and columns
  4. execute_query - Safely executes BigQuery SQL queries with automatic LIMIT clause insertion and cost control
  5. check_query_scan_amount - Retrieves the scan amount for BigQuery SQL queries

Installation and Environment Setup

Prerequisites

  • Python 3.11 or later
  • Google Cloud Platform account
  • GCP project with BigQuery API enabled

Installing Dependencies

This project uses uv for package management:

# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

Configuring Option

For a list of configuration values, see:

docs/settings.md

MCP Setting

{
    "mcpServers": {
        "bq_mcp_server": {
            "command": "uv",
            "args": [
                "run",
                "--directory",
                "<your install directory>",
                "mcp_server"
            ],
            "env": {
                "PYTHONPATH": "<your install directory>",
                "PROJECT_IDS": "<your project id>"
            }
        }
    }
}

Running Tests

Running All Tests

pytest

Running Specific Test Files

pytest tests/test_logic.py

Running Specific Test Functions

pytest -k test_function_name

Checking Test Coverage

pytest --cov=bq_mcp_server

Local Development

Starting the MCP Server

uv run mcp_server

Starting the FastAPI REST API Server

uvicorn bq_mcp_server.adapters.web:app --reload

Development Commands

Code Formatting and Linting

# Code formatting
ruff format

# Linting checks
ruff check

# Automatic fixes
ruff check --fix

Dependency Management

# Adding new dependencies
uv add <package>

# Adding development dependencies
uv add --dev <package>

# Updating dependencies
uv sync

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bq_mcp_server-0.1.0.tar.gz (44.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bq_mcp_server-0.1.0-py3-none-any.whl (36.6 kB view details)

Uploaded Python 3

File details

Details for the file bq_mcp_server-0.1.0.tar.gz.

File metadata

  • Download URL: bq_mcp_server-0.1.0.tar.gz
  • Upload date:
  • Size: 44.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.6

File hashes

Hashes for bq_mcp_server-0.1.0.tar.gz
Algorithm Hash digest
SHA256 49387a78519cdac6fe7585cd577aa19ffd74d7f25c0596552265b14ed2dccda1
MD5 0995d7c398d22dc8bf5b1203aec16bc6
BLAKE2b-256 3ac0f593277f186dec169f693433268e4464470081c1f645b597b303810a5077

See more details on using hashes here.

File details

Details for the file bq_mcp_server-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for bq_mcp_server-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 99ba184d5a52c099e8ca6363ff904f5de81f6e2890194ebf5bc37b0e61bc1d50
MD5 57ee0aae26c85780bd0bebabcffaffa4
BLAKE2b-256 d4a54866941ff89ef80fd9d514927e014b6ccc0219c35f6522b74e405176f2b6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page