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igot-ai

Datalog Studio MCP Server

by igot-ai

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.

Related MCP server: Laminar MCP Server

Quick Start

1. Prerequisites

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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