base-metadata-extractor

Extract Airtable base metadata by using MCP connectors to discover tables, fields, and configurations first. Then optionally run browser-console scripts to capture automation definitions and interface layouts, gathering all results into a unified data model for downstream documentation.

The best way to document Airtable schema is extracting complete base metadata via MCP using list_tables_for_base and get_table_schema commands. This captures tables, fields, types, and configurations, feeding the results directly into design docs, health checks, or training guides.

Yes, this metadata extraction approach supports Airtable automations and interfaces by utilizing optional browser-console scripts. These scripts capture automation definitions and interface layouts alongside MCP-based schema discovery for deeper analysis and documentation.

Yes, you can use MCP to extract tables, fields, and configurations from an Airtable base. The extraction workflow utilizes MCP connectors like list_tables_for_base, get_table_schema, and list_records_for_table to pull complete schema configurations.

Feed extracted Airtable schema into technical design docs and health checks by gathering the MCP schema results and optional browser-script captures. This collected data model is then passed directly into consuming skills built for generating SA-grade deliverables.

A limitation of extracting Airtable metadata via MCP and browser scripts is that automation and interface data capture depends on running browser-console scripts manually. This optional step requires a browser environment, unlike the automated MCP-based schema extraction process.