OpenMetadata MCP Server

Overview

The mcp-server-openmetadata is a Model Context Protocol (MCP) server that bridges OpenMetadata’s rich REST API with AI assistants. By exposing a standardized MCP interface, it lets Claude or any MCP‑compliant client query, create, update, and delete metadata objects—such as tables, databases, dashboards, pipelines, and more—without needing to write custom integrations. This solves the common pain point of having to translate between proprietary OpenMetadata endpoints and the conversational flow expected by AI assistants.

For developers, this server provides a single entry point for all core metadata entities. Each entity type (e.g., , , ) has a full set of CRUD operations, and the server guarantees consistent response schemas. This uniformity allows AI agents to retrieve contextual information about data assets, discover lineage, or suggest new tables with minimal friction. The value lies in turning complex metadata queries into simple conversational prompts that the AI can resolve automatically.

Key capabilities include:

• Comprehensive entity coverage: Tables, databases, schemas, dashboards, charts, pipelines, topics, metrics, containers, and more are all available through the same protocol.
• Full CRUD support: Clients can list, fetch by ID or fully‑qualified name (FQN), create, update, and delete resources.
• RESTful mapping: The server internally maps MCP actions to the corresponding OpenMetadata REST endpoints, preserving authentication and pagination semantics.
• Extensibility: New entity types can be added by extending the mapping layer, making it future‑proof as OpenMetadata evolves.

Typical use cases span data engineering and analytics workflows. A data engineer might ask an AI assistant to “show me all tables in the database” or “create a new pipeline for nightly ETL.” An analyst could request “list dashboards that reference the metric” or “update the chart description.” In both scenarios, the AI can directly manipulate metadata through MCP calls, streamlining governance and collaboration.

Integration with existing AI workflows is straightforward: the MCP server acts as a backend service that an assistant’s toolchain calls. The assistant sends a structured request (e.g., list tables), receives the standardized response, and presents it conversationally. Because MCP abstracts away authentication details, developers can focus on building higher‑level prompts and logic rather than handling API quirks. The result is a smoother, more reliable AI‑driven data catalog experience that scales with an organization’s metadata footprint.