Apache Gravitino(incubating)

Overview

Apache Gravitino is a unified metadata catalog that simplifies data governance across multiple lakehouse and data‑lake platforms. The MCP server for Gravitino bridges the gap between this powerful catalog and AI assistants by exposing a lightweight, standardized interface for metadata operations. Developers can now let an assistant like Claude query catalog information, list tables or schemas, and manage roles—all without writing custom connectors.

The server is built on FastMCP, a minimal framework that translates MCP calls into concrete API requests. Once configured with the Gravitino endpoint, metalake name, and authentication credentials (JWT or basic), the server automatically registers a suite of tools covering common metadata tasks: listing catalogs, schemas, tables, models; retrieving table definitions; and managing users, roles, tags, and permissions. This abstraction allows an assistant to invoke a single “get list of tables” command and receive structured JSON back, which can then be used in downstream reasoning or data‑driven workflows.

Key capabilities include:

• Comprehensive catalog interaction – Full CRUD support for catalogs, schemas, tables, and models, enabling assistants to discover available datasets on demand.
• User‑role management – Tools for listing users, roles, and assigning permissions, which is essential in regulated data environments.
• Tagging and metadata enrichment – Ability to query and apply tags, making it easier for assistants to surface data lineage or quality annotations.
• Secure authentication – Dual support for JWT and basic auth ensures that the server can operate in both cloud‑native and legacy setups.

Typical use cases involve data scientists exploring a shared lakehouse: an assistant can fetch the latest table schema, suggest relevant transformations, or even generate SQL snippets that respect existing role constraints. Data engineers can automate catalog updates by having an assistant trigger “create table” or “update schema” commands based on workflow events. Compliance teams benefit from role‑based queries that list all users with read access to sensitive datasets, enabling quick audit checks.

Integration into AI workflows is straightforward. The MCP server exposes each operation as a tool that the assistant can call with natural language prompts. Because FastMCP handles serialization and error mapping, developers need not write custom adapters; they simply configure environment variables, launch the server, and let the assistant orchestrate metadata tasks as part of larger data‑pipeline or conversational flows. This tight coupling reduces friction, accelerates prototyping, and ensures that metadata remains consistent across automated processes.