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Model Context Protocol (MCP)

Marmot includes a built-in Model Context Protocol (MCP) server that enables AI assistants like Claude, ChatGPT and other LLM-powered tools to interact with your data catalog using natural language.

What is MCP?​

The Model Context Protocol is a standardised way for AI assistants to connect with external data sources and systems. Think of it as a universal translator between AI models and your data. It exposes your catalog's capabilities, metadata and functions through machine-readable schemas that AI assistants can understand and use.

With MCP, you can ask questions like:

  • "What tables does the analytics team own?"
  • "Show me all BigQuery datasets tagged as 'production'"
  • "Find the upstream dependencies for the user_events table"
  • "Who owns the payment processing API?"

How It Works​

Marmot's MCP server exposes your data catalog's metadata in real-time, enabling AI assistants to:

  1. Search Assets - Query your catalog using natural language
  2. View Lineage - Explore upstream and downstream dependencies
  3. Read Metadata - Access descriptions, owners, tags and custom metadata
  4. Discover Context - Understand relationships between assets

Authentication​

MCP uses the same authentication as Marmot's REST API. You'll need an API key to connect:

  1. Navigate to your user profile in Marmot
  2. Go to Settings → API Keys
  3. Generate a new API key
  4. Use this key in your MCP client configuration

The AI assistant will have the same permissions as your user account, respecting all role-based access controls.

Getting Started​

Choose your AI assistant to see configuration examples:

  • Claude Desktop - Anthropic's official desktop application
  • Claude Code - Claude's command-line interface
  • Cursor - The AI-first code editor
  • Cline - VS Code extension for AI-powered coding
  • LibreChat - Open-source ChatGPT alternative

Available Tools​

Marmot's MCP server provides the following tools to AI assistants:

discover_data​

Unified data discovery for finding any asset in the catalog. Supports natural language queries, specific lookups by ID or MRN (qualified identifiers like postgres://db/schema/table), filtering by type/provider/tags and metadata-based queries.

Returns asset details including ownership, schema and lineage information.

find_ownership​

Bidirectional ownership queries to answer "Who owns this asset?", "What does this user own?" and "Show me all data owned by the data-eng team". Works for both data assets and glossary terms. Can query by asset ID, user ID/username or team ID/team name.

lookup_term​

Business glossary lookups for understanding terminology and definitions. Search for glossary terms by name or retrieve specific term definitions. Returns term details, ownership, related terms and parent/child relationships in the glossary hierarchy.

Example Queries​

Once configured, you can interact with Marmot through natural language:

  • "Find all Kafka topics owned by the data-platform team"
  • "What are the upstream dependencies for the analytics.user_events table?"
  • "What does 'Monthly Active Users' mean in our glossary?"
  • "Show me all BigQuery datasets tagged as production"
  • "Who owns the customer_data table?"

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