OpenMetadata

Supports Mcp

AI systems need more than data access. They need governed context, business meaning, trust signals, lineage, usage, ownership, standards, and organizational memory.

A direct connection to a warehouse, lake, dashboard, or pipeline exposes raw structures. It does not tell an AI assistant what the data means, whether it is certified, who owns it, which policies apply, what contract governs it, what breaks if it changes, or what the organization has already learned about it.

OpenMetadata is the open context layer that gives every data user and AI agent the full picture of enterprise data.

OpenMetadata brings together five capabilities:

1. Context — technical, operational, trust, usage, and lineage metadata from across the data ecosystem.
2. Semantics — business meaning through glossaries, metrics, classifications, domains, policies, ontologies, and data products.
3. Knowledge Graph — relationships connecting assets, columns, people, teams, quality, lineage, policies, memories, contracts, and business concepts.
4. Memory — conversations, AI threads, decisions, assumptions, runbooks, remediation notes, and reusable memory nuggets that preserve tribal knowledge.
5. Activation — MCP, Semantic Search, APIs, SDKs, events, and workflows that make context usable by AI assistants, agents, applications, and humans.

With OpenMetadata, users and AI agents can answer:

• What does this metric mean and how is it calculated?
• Which datasets power this dashboard?
• Who owns this data product?
• Which data contract applies?
• Is this dataset fresh, tested, certified, and trusted?
• Which downstream dashboards, pipelines, or ML models are affected by this column change?
• Which columns contain sensitive customer information?
• Which glossary terms, policies, standards, and business concepts apply?
• What decisions, assumptions, incidents, or conversations have already been captured about this asset?

The OpenMetadata graph does not only store data assets. It stores the relationships between assets, columns, owners, teams, policies, quality tests, lineage, classifications, glossary terms, metrics, domains, data contracts, data products, conversations, and memory nuggets.

Example relationships:

Table               ──hasColumn────────────> Column
Column              ──classifiedAs─────────> PII
Column              ──represents───────────> Customer Identifier
Table               ──ownedBy─────────────> Data Engineering Team
Table               ──partOf──────────────> Customer 360 Data Product
Dashboard           ──dependsOn───────────> Table
Metric              ──definedBy───────────> Glossary Term
Pipeline            ──produces────────────> Table
Column              ──flowsTo─────────────> Column
Test Case           ──validates───────────> Table
Policy              ──governs─────────────> Classification
Data Contract       ──appliesTo───────────> Table
OpenLineage Event   ──updatesLineageFor───> Pipeline
Agent Conversation  ──capturedAs──────────> Memory 
Memory              ──informs─────────────> Data Product
Memory              ──documentsDecisionFor> Metric
Memory              ──attachedTo──────────> Table / Column / Topic / Dashboard / Pipeline / API

This graph gives AI systems the relationships, meaning, memory, and governance they need to reason across the data estate.