{
  "markdown": "# Catalog MCP Extension\n\nProfessional MCP server for integrating Catalog tasks into the Gemini CLI. Manage data catalogs, collections, and master data using natural language.\n\n## Features\n\n- **Catalog Discovery**: List and find data catalogs within your workspace.\n- **Master Data Management**: Explore collections, attributes, and AI prompt templates.\n- **Data Asset Control**: List uploaded documents and analyze data structures.\n- **Data Ingestion**: Direct data ingestion with automated AI transformation.\n\n## Quick Start\n\n### 1. Prerequisites\n\n- [Node.js](https://nodejs.org) (v18+) and npm installed.\n\n### 2. Installation\n\nInstall the extension and its dependencies:\n\n```bash\nnpm run install-deps\nnpm run build\ngemini extensions install .\n```\n\n### 3. Configuration\n\nThe extension requires a `DATALOG_API_KEY`. By default, it connects to `https://studio.igot.ai/v1/catalog`. \n\nFor custom enterprise installations, you can configure the endpoint using:\n- `DATALOG_API`: The domain endpoint (e.g., `https://enterprise.com`).\n- `CATALOG_URI`: The API path suffix (e.g., `/v1/catalog`).\n\n## Development\n\nUse the provided scripts for a professional development workflow:\n\n- `npm run dev`: Start MCP server in watch mode.\n- `npm run lint`: Run ESLint to find and fix issues.\n- `npm run format`: Format code with Prettier.\n- `npm run typecheck`: Run TypeScript type checking.\n- `npm run preflight`: Run a full cleanup, install, lint, and build cycle.\n\n## Tools Summary\n\n- `list_catalogs()`: List all accessible data catalogs.\n- `list_collections(catalog_id)`: List collections in a specific catalog.\n- `list_attributes(catalog_name, collection_name)`: View collection schema and attributes.\n- `list_data_assets(catalog_name, collection_name)`: List uploaded files within a collection.\n- `ingest_data(catalog_name, collection_name, text, transform?)`: Ingest master data into a collection.\n\n",
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  "repo_slug": "igot-ai/datalog-studio",
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}