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OpenMetadata

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Open context layer delivering trusted data semantics for AI agents.

MCP ServerAI & KnowledgeLocal (stdio) 14.2k
View on GitHub
Updated 2026-06-15

What is the OpenMetadata MCP server?

OpenMetadata aggregates technical metadata, data quality results, column-level lineage, glossaries, policies, and conversations into one searchable graph. This context helps AI systems determine what data exists, whether it is trusted, and how it relates to business concepts without raw connector access.

The platform activates this knowledge through semantic search, APIs, SDKs, and its MCP server so agents can answer questions about ownership, freshness, impact, and compliance. It preserves organizational memory such as decisions and runbooks alongside the metadata.

Install & connect

Set up this server, then add it to your MCP client.

Full setup instructions are in the GitHub repository.

Example prompts

Once connected, try asking your AI client:

“Which datasets power the revenue dashboard and who owns them?
“Is the customer table certified and what glossary terms apply to the email column?
“Show column-level lineage for the orders fact table and list any downstream ML models.
“What data contracts or policies govern the marketing domain datasets?

Security & permissions

Runs locally over stdio and requires network or local access to an OpenMetadata instance; may need API credentials or service account tokens to read metadata and memory stores.

What you can do with OpenMetadata

Trusted data discovery

AI agents query OpenMetadata to locate certified datasets, understand column meanings via glossaries, and verify quality signals before using data in analysis.

Impact and lineage analysis

When a column changes, agents retrieve upstream and downstream lineage to identify affected dashboards, models, and pipelines along with responsible owners.

Policy-aware reasoning

Agents check data contracts, classifications, and access policies stored in OpenMetadata to ensure responses respect governance rules and avoid sensitive fields.

How to use OpenMetadata

  1. 1Install and run the OpenMetadata server with the MCP component enabled.
  2. 2Configure connection details and any required credentials in the MCP server config.
  3. 3Start the MCP server using stdio transport in your AI client settings.
  4. 4Verify the server can reach the OpenMetadata API and knowledge graph.
  5. 5Test with sample prompts to confirm context retrieval works as expected.

OpenMetadata: pros & cons

Pros

  • +Comprehensive unified graph of metadata, lineage, quality, and business semantics
  • +Strong focus on AI memory and governed context rather than raw data access
  • +130+ connectors and open standards reduce integration effort
  • +Preserves tribal knowledge through conversations and runbooks

Cons

  • –Requires a running OpenMetadata deployment and ongoing metadata ingestion
  • –MCP tool surface is not detailed in public documentation
  • –Performance depends on the size and freshness of the metadata graph
Did you find this helpful?

Frequently asked questions

No, it supplies the surrounding context, lineage, and semantics so AI can reason safely; raw data access still occurs through other connectors.

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