Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery
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# BigQuery MCP Server [](https://www.python.org/) [](https://fastapi.tiangolo.com/) 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 - **File Export**: Execute queries and save results to local files in CSV or JSONL format - **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 6. `save_query_result` - Executes BigQuery SQL queries and saves results to local files (CSV or JSONL format) ### Tool Details #### `save_query_result` The `save_query_result` tool provides advanced query execution with file export capabilities: **Parameters:** - `sql` (required): SQL query to execute - `output_path` (required): Local file path to save results - `format` (optional): Output format - `"csv"` (default) or `"jsonl"` - `project_id` (optional): Target GCP project ID - `include_header` (optional…
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