Dremio MCP

Dremio is a lakehouse query engine that puts a SQL and semantic layer over data sitting in object storage and external sources. The official MCP server connects an AI client to a Dremio Cloud or Dremio Software deployment so an agent can find the right dataset and query it, instead of depending on a person to navigate the catalog and hand over a table path first. The server exposes catalog discovery, so an agent can walk sources, spaces, folders, tables, and views and read their column schemas and types. Semantic search runs over dataset names, descriptions, and wiki documentation, which is how an agent locates the correct table when it does not know where the data lives. SQL execution submits a query to the Dremio engine and returns job status plus structured result rows, and job metadata explains how a query ran, which matters when an assistant needs to say why a result was slow or which reflection served it. Connections use a personal access token, so the agent sees only what that user is entitled to under Dremio access policies, including row and column level rules. It targets analysts and data engineers who want an assistant to answer ad hoc questions over the lakehouse, and teams building agents that need governed SQL access to a semantic layer rather than a raw warehouse credential.