BigQuery MCP server optimized for quick navigation of larger projects and datasets.
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# 🗂️ BigQuery MCP Server Practical MCP server for navigating BigQuery datasets and tables by LLMs. Designed for larger projects with many datasets/tables, optimized to keep LLM context small while staying fast and safe. - **Minimal by default**: list datasets and tables names; fetch details only when asked - **Navigate larger projects**: filter by name, request detailed metadata/schemas on demand - **Quick table insight**: optional schema, column descriptions and fill-rate to help an agent decide relevance fast - **Safe to run**: read-only query execution with guardrails (SELECT/WITH only, comment stripping) - **Supports vector search**: Use bigquery as your vector store. See [Vector Search](#-vector-search-optional) section for full setup instructions. ## Quick Start **Prerequisites:** Python 3.10+ and [uv](https://github.com/astral-sh/uv) package manager ### 🚀 Quick Setup **Option 1: Direct from PyPI (Recommended)** ```bash # 1. Authenticate gcloud auth application-default login # 2. Run server uvx bigquery-mcp --project YOUR_PROJECT --location US ``` **Option 2: Clone locally (development setup)** ```bash # 1. Clone and setup git clone https://github.com/pvoo/bigquery-mcp.git cd bigquery-mcp # 2. Configure environment cp .env.example .env # Edit .env with your project and location # 3. Run or inspect make run # Start server make inspect # Open MCP inspector ``` ### 🔧 MCP Client Configuration **Option 1: PyPI package (Recommended)** Simplest setup using the published PyPI package: ```json { "mcpServers": { "bigquery": { "command": "uvx", "args": [ "bigquery-mcp", "--project", "your-project-id", "--location", "US" ] } } } ``` **Option 2: Local clone (for development)** ```bash # Clone first git clone https://github.com/pvoo/bigquery-mcp.git ``` ```json { "mcpServers": { "bigquery": { "command": "uv", "args": ["--directory", "/absolute/path/to/bigquery-mcp", "run", "bigque…
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