Why it’s hereStarts in our sandbox (needs your API keys) · 148 GitHub stars
Server implementation for Google BigQuery integration that enables direct BigQuery database access and querying capabilities
The best MCP servers for Google BigQuery — let AI agents explore datasets, inspect schemas, and run SQL against your data warehouse.
Why it’s hereStarts in our sandbox (needs your API keys) · 148 GitHub stars
Server implementation for Google BigQuery integration that enables direct BigQuery database access and querying capabilities
Why it’s here550 GitHub stars
MCP and MCP SSE Server that automatically generate API based on database schema and data. Supports PostgreSQL, Clickhouse, MySQL, Snowflake, BigQuery, Supabase
Why it’s hereMatches this topic; not yet verified by us
MCP Server for natural language querying of Snowflake, Google BigQuery, and DataBricks Data Products through Alkemi.ai.
Why it’s here131 GitHub stars
BigQuery database integration with schema inspection and query capabilities
Why it’s hereMatches this topic; not yet verified by us
Universal database MCP server supporting multiple database types including PostgreSQL, Redshift, CockroachDB, MySQL, RDS MySQL, Microsoft SQL Server, BigQuery, Oracle DB, and SQLite.
Why it’s hereTools confirmed live over MCP
Read-only BigQuery tools for plain-language data questions, with a cost gate on every query
Why it’s hereTools confirmed live over MCP
Pre-flight query cost & result-size guardrails for AI agents across BigQuery and Snowflake
Why it’s hereMatches this topic; not yet verified by us
Production MCP server for Postgres, Oracle, Snowflake, BigQuery, Redshift, DuckDB, MotherDuck.
Why it’s hereMatches this topic; not yet verified by us
MCP server for Databricks, BigQuery, PostgreSQL, MySQL, SQL Server, SQLite, MariaDB
BigQuery bills by bytes scanned, so an agent writing exploratory queries can get expensive. Prefer servers that dry-run queries or cap maximum bytes billed, and point them at partitioned tables.
Use a service account with BigQuery Data Viewer and Job User roles on specific datasets instead of project-wide editor access.
This page ranks BigQuery MCP servers with servers we have seen run first, then by popularity. Prefer servers that support dry runs or byte limits to keep query costs predictable.
Set a maximum bytes billed limit, give the service account access only to needed datasets, and use a server that dry-runs queries to estimate cost before executing.
Usually a Google Cloud service account key or application default credentials, with read and job-run roles on the datasets the agent should query.