Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Datalog Studio MCP ServerShow me the schema for the users table in the Analytics project"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Catalog MCP Extension
Professional MCP server for integrating Catalog tasks into the Gemini CLI. Manage data catalogs, collections, and master data using natural language.
Features
Catalog Discovery: List and find data catalogs within your workspace.
Master Data Management: Explore collections, attributes, and AI prompt templates.
Data Asset Control: List uploaded documents and analyze data structures.
Data Ingestion: Direct data ingestion with automated AI transformation.
Quick Start
1. Prerequisites
Node.js (v18+) and npm installed.
2. Installation
Install the extension and its dependencies:
npm run install-deps
npm run build
gemini extensions install .3. Configuration
The extension requires a DATALOG_API_KEY. By default, it connects to https://studio.igot.ai/v1/catalog.
For custom enterprise installations, you can configure the endpoint using:
DATALOG_API: The domain endpoint (e.g.,https://enterprise.com).CATALOG_URI: The API path suffix (e.g.,/v1/catalog).
Development
Use the provided scripts for a professional development workflow:
npm run dev: Start MCP server in watch mode.npm run lint: Run ESLint to find and fix issues.npm run format: Format code with Prettier.npm run typecheck: Run TypeScript type checking.npm run preflight: Run a full cleanup, install, lint, and build cycle.
Tools Summary
list_catalogs(): List all accessible data catalogs.list_collections(catalog_id): List collections in a specific catalog.list_attributes(catalog_name, collection_name): View collection schema and attributes.list_data_assets(catalog_name, collection_name): List uploaded files within a collection.ingest_data(catalog_name, collection_name, text, transform?): Ingest master data into a collection.
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