Claude Code + Data Catalog Agent: Self-Maintaining Metadata from Your Terminal

The Claude Code data catalog agent is an MCP server from Data Workers that answers questions about your data — what tables exist, what they contain, who owns them, how they connect — from your terminal. It maintains a live, queryable catalog of your entire data estate without manual documentation.

The Claude Code data catalog agent gives you instant, accurate answers about your data — what tables exist, what they contain, who owns them, and how they connect — without ever opening a catalog UI. If you have ever searched your data catalog for a table and found outdated documentation from two years ago, or worse, no documentation at all, the data catalog agent from Data Workers solves that problem. It is an MCP server that maintains a live, queryable catalog of your entire data estate, accessible through natural language in Claude Code.

Enterprise data catalogs cost $200K-$500K per year and still suffer from the same fundamental problem: they depend on humans to keep metadata current. Data engineers are supposed to document every new table, update descriptions when schemas change, and maintain lineage mappings. In practice, documentation decays within weeks of creation. The data catalog agent eliminates this decay by generating metadata directly from your infrastructure — not from human memory.

Why Traditional Data Catalogs Fail

The catalog problem is not a tooling problem — it is an incentive problem. The person who benefits from documentation (the analyst searching for data) is not the person who writes it (the engineer who built the pipeline). Engineers are measured on shipping pipelines, not on writing descriptions. So documentation is perpetually incomplete, outdated, or both.

Studies show that 40-60% of data catalog entries are outdated at any given time. That means when you search your catalog, there is a coin-flip chance that what you find is wrong. This uncertainty erodes trust to the point where experienced analysts skip the catalog entirely and ask colleagues on Slack — which is the most expensive search engine in existence.

Querying Your Data Estate in Natural Language

With the data catalog agent connected to Claude Code, you can ask questions about your data in plain English:

claude "What tables contain revenue data and which ones are the source of truth?"

The agent does not just search table names for the word "revenue." It searches across column names, column descriptions, dbt model documentation, query patterns, lineage relationships, and data samples to find every table related to revenue. It then ranks them by authority — identifying the source-of-truth tables versus derived or downstream tables.

• •`fct_revenue` (finance mart): Source of truth for revenue reporting. Updated hourly. Owner: Finance Data Team. Contains net revenue, gross revenue, and recognized revenue by day and segment.
• •`stg_stripe__charges` (staging): Raw Stripe charge data, upstream of fct_revenue. Not suitable for direct reporting — use fct_revenue instead.
• •`rpt_exec_revenue` (reporting): Executive summary derived from fct_revenue. Updated daily. Consumed by the board dashboard.
• •`raw.salesforce.opportunities` (raw): Salesforce opportunity data with an amount field. This is pipeline revenue (forecasted), not actual revenue. Often confused with actual revenue.

That last point — the distinction between pipeline revenue and actual revenue — is exactly the kind of context that prevents costly mistakes. The agent knows this because it has analyzed query patterns and lineage, not because someone remembered to document it.

Self-Maintaining Metadata

The data catalog agent generates and maintains metadata automatically by combining multiple signals:

• •Schema metadata from your warehouse — table names, column names, data types, constraints
• •dbt documentation — model descriptions, column descriptions, tests, and tags
• •Query history — which tables are queried most, by whom, and in what context
• •Lineage — how data flows from source to consumption, column-level
• •Freshness — when each table was last updated and whether it meets SLA expectations
• •Usage patterns — which tables are actively used versus abandoned

When a schema changes, the catalog updates automatically. When a new table is created, it is profiled and classified within minutes. When a table stops being queried, it is flagged as potentially abandoned. There is no manual curation required.

Discovery Workflows from Your Terminal

The catalog agent supports the full range of data discovery workflows:

• •claude "What data do we have about customer churn?" — semantic search across all metadata
• •claude "Who owns the orders table and when was it last modified?" — ownership and freshness lookup
• •claude "Show me the lineage of fct_revenue — all the way back to raw sources" — full lineage traversal
• •claude "What tables were created in the last week?" — new data discovery
• •claude "Are there any undocumented tables in the production schema?" — documentation gap analysis
• •claude "What is the difference between customer_id in the users table and the orders table?" — cross-table entity resolution

Each of these queries would require navigating multiple pages in a traditional catalog UI, assuming the information is there at all. In Claude Code, they are one-liners that return comprehensive answers.

Before and After: Data Discovery

Integration with Other Data Workers Agents

The data catalog agent becomes more powerful when combined with other agents in the Data Workers platform. The quality monitoring agent enriches catalog entries with quality scores and anomaly history. The governance agent adds PII classification and access control information. The cost optimization agent adds usage statistics and cost attribution.

When you ask about a table, you get the complete picture — not just schema and documentation, but quality status, governance classification, cost profile, and usage patterns. This unified view is only possible because all 15 agents share a common context layer through the Data Workers platform.

Getting Started with the Data Catalog Agent

The data catalog agent connects to Snowflake, BigQuery, Redshift, Databricks, and integrates with dbt, Looker, and other data tools. Follow the Getting Started guide to install Data Workers and the Claude Code Setup guide for MCP configuration. The agent begins profiling your data estate immediately after connection.

Check the Docs for advanced features including custom metadata enrichment, catalog export, and integration with existing catalog tools. Visit the Product page to see all 15 agents.

Your data catalog should work for you, not the other way around. Book a demo to see self-maintaining metadata in action on your own data stack.