Cataloging tables, topics, queues, and APIs across fragmented data stacks usually demands heavyweight enterprise governance suites, yet Marmot delivers fast full-text asset discovery and AI-ready metadata context in a single self-hosted service. Data engineering and platform teams use its 71 official plugins to automatically ingest schemas, run histories, and dependencies from Apache Kafka, Airflow, Iceberg, Superset, PostgreSQL, BigQuery, and AsyncAPI specifications without moving raw table rows or message payloads. From the web interface, analysts can run structured boolean queries, filter assets by custom tags or PII classifications, and inspect column-level schemas in seconds. Interactive upstream and downstream lineage graphs let engineers trace how events flow from message brokers into warehouse tables and BI dashboards so they can assess what breaks before renaming a column or deprecating an endpoint. Shared business glossaries, bidirectional ownership assignments, and curated data product bundles ensure every department speaks the same language and knows exactly which team maintains each pipeline. Through the built-in Model Context Protocol server, AI assistants in Cursor, Claude Desktop, and LibreChat can invoke tools like discover_data, find_ownership, and lookup_term to answer natural-language questions against live catalog metadata instead of guessing. Platform administrators can also manage catalog resources declaratively using the REST API, CLI, Terraform provider, or Pulumi integration. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.