Hi @maikel ,

Custom hosted MCP servers are implemented as a Databricks App - the documentation here should help you get started: https://docs.databricks.com/aws/en/generative-ai/mcp/custom-mcp

To answer some of your questions more specifically: 

• Tables: If you want to just access tables, you can use managed DBSQL MCP server to run SQL across Unity Catalog tables, or use the managed Genie MCP server for natural-language queries over structured data. You can also expose deterministic tools as Unity Catalog Functions and use the managed UC Functions MCP server to run them.

• Volumes: Databricks does not currently expose Unity Catalog Volumes as first‑class managed MCP tools, but it may be something in the future. Today, you can either model the needed actions as Unity Catalog Functions (e.g., file read/parse helpers) or host a custom MCP server on Databricks Apps that implements volume operations under your own API contract.

• Custom MCP auth: OAuth
• Managed and external: OAuth or Personal Access Token (PAT)
  • Auth Source: https://docs.databricks.com/aws/en/generative-ai/mcp/connect-external-services
• Authorization is tied to Unity Catalog: Managed MCP enforces UC privileges for the calling identity (on‑behalf‑of‑user), so tool discovery and execution only include what the user can see and run. Grant USE CATALOG/USE SCHEMA/SELECT/EXECUTE as appropriate on the resources your agent needs.

Should you build a custom MCP server or use Managed MCP?
My recommendation would be to start with managed because it should be easier to get the ball rolling. If you find that you can't do everything with managed, you can switch to custom.