dbt-mcp and bigquery-mcp
These are complements: dbt transforms and models data in BigQuery, while the BigQuery MCP server provides safe read-only query access to those transformed datasets for LLMs, creating a complete pipeline from data transformation to AI consumption.
About dbt-mcp
dbt-labs/dbt-mcp
A MCP (Model Context Protocol) server for interacting with dbt.
Exposes dbt project metadata and operations through 40+ tools across Discovery API, Semantic Layer, SQL execution, and dbt CLI capabilities—enabling AI agents to query lineage, model details, metrics, and trigger jobs. Connects to dbt Core, Fusion, and Platform environments, supporting both local manifest inspection and cloud-based operations with optional column-level lineage analysis via the Fusion engine.
About bigquery-mcp
pvoo/bigquery-mcp
Practical MCP server for large BigQuery datasets. Supports vector search. Keep LLM context small while staying fast and allowing only safe read-only actions.
Implements MCP (Model Context Protocol) as a stdio-based server with dual-mode tools that minimize token usage by defaulting to lightweight responses—basic dataset listings return names only, detailed metadata loads on-demand. Enforces read-only safety through query validation (SELECT/WITH statements only) and automatic cost tracking with a ~$0.50 per-query billing cap. Integrates with BigQuery's Vertex AI embeddings for semantic vector search and supports dataset access control via allowlisting.
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