You can’t get value out of your data from AI tools if AI can’t understand your data!
Companies are racing to deploy AI agents and large language models, yet many overlook a critical factor: AI is only as reliable as the metadata it receives. Without clear, maintained metadata, even advanced tools produce incorrect joins, misinterpret fields, and deliver flawed insights.
The Model Context Protocol (MCP)—the open standard launched by Anthropic in November 2024—changes the game. MCP servers provide secure, standardized connections between AI clients (like Claude) and enterprise systems, including databases and data catalogs. This lets AI query data in real time, much like a human analyst. However, MCP servers deliver full value only when backed by high-quality metadata.
Companies must now treat metadata maintenance as core infrastructure. Fully populating and updating potentially three layers (Depending on your database and platforms) —INFORMATION_SCHEMA, ISO/IEC 11179, and enterprise data catalogs—has become non-negotiable for maximizing AI and MCP effectiveness.
Layer 1: INFORMATION_SCHEMA – The Structural Foundation
The ANSI/ISO SQL standard INFORMATION_SCHEMA offers machine-readable details on tables, columns, data types, and—crucially—foreign-key constraints. These constraints define precise join paths, such as orders.customer_id = customers.id.
Many teams ignore this built-in resource, leaving foreign keys undocumented and comments empty. As a result, AI agents generate faulty SQL with wrong joins.
Solution: Treat schema hygiene as routine. Enforce foreign keys, add clear column comments, and securely expose INFORMATION_SCHEMA through your MCP server. This low-effort step dramatically improves query accuracy with almost no cost.
Layer 2: ISO/IEC 11179 – The Semantic Foundation
Structure shows how tables connect. Semantics explain why a field exists and how to use it correctly.
ISO/IEC 11179, the international standard for metadata registries, defines data elements with business definitions, value domains, stewardship, and usage rules. A vague status_code becomes clearly documented as “order lifecycle stage: 01=Pending, 02=Shipped, 03=Cancelled—do not use for payment status.”
Without this layer, AI misinterprets fields and applies incorrect logic. When MCP servers pull ISO 11179 definitions alongside schema data, agents gain true business context, not just raw structure.
Maintaining a semantic registry (often inside your catalog) is now essential. It equips AI to ask and receive authoritative answers about field meaning and rules.
Layer 3: The Enterprise Data Catalog – The Unified AI Brain
Modern data catalogs (such as OpenMetadata, DataHub, Collibra, or Alation) combine INFORMATION_SCHEMA structure, ISO 11179 semantics, lineage, usage stats, ownership, quality scores, and sensitivity tags.
Leading catalogs now integrate natively with MCP. AI agents can discover popular joins, check data quality, respect PII policies, and access the business glossary—all in one place.
A stale or incomplete catalog turns MCP into a fast pipeline for poor context. Rich, living metadata transforms it into a powerful enabler of trustworthy AI.
The Payoff: Higher ROI from AI
Organizations investing in metadata should see clear gains:
- AI-generated SQL succeeds far more often on the first attempt.
- Built-in governance reduces compliance risks.
- Onboarding for new agents and analysts accelerates dramatically.
- Safe cross-silo analysis becomes possible because context is reliable.
Neglecting metadata wastes AI investments and slows production deployments. MCP amplifies both the benefits of good metadata and the costs of poor metadata.
90-Day Action Plan
- Weeks 1–2: Audit and strengthen INFORMATION_SCHEMA. Add comments, enforce keys, and expose it via MCP.
- Weeks 3–6: Populate ISO 11179 definitions for priority domains (customer, finance, operations). Assign stewards.
- Weeks 7–10: Upgrade to an MCP-compatible catalog. Import schema and semantic data; activate usage analytics.
- Weeks 11–12: Test with live AI agents. Compare query success rates with and without enriched metadata.
- Ongoing: Automate updates through CI/CD, dbt, and quality gates. Include metadata tasks in team goals.
Most required capabilities already exist in your environment. The work is mainly about consistent, disciplined use.
Final Word
MCP and AI agents are powerful multipliers—but they multiply the quality of your metadata. To unlock accurate, auditable, high-impact AI that truly drives business value, stop treating metadata as optional documentation.
Fully maintain your INFORMATION_SCHEMA, ISO/IEC 11179 registry, and data catalog (As applicable of course) . Expose them through MCP servers. Do it now, to allow your investments in AI to pay off.
Your AI systems—and your company—will benefit immediately.
(This Blog post was formatted and developed with the assistance of Grok)