What Is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard for defining how AI language models interact with external tools, data sources, and services. Developed by Anthropic and rapidly adopted across the AI ecosystem, MCP provides a standardized way to expose capabilities (called tools) to AI models — each tool has a name, a description (in natural language that the AI model reads to understand what the tool does), and a parameter schema (defining what inputs the tool accepts).

When an AI model with MCP access is given a task, it can autonomously decide which tools to call, with which parameters, and in which sequence — forming a multi-step agentic workflow without human guidance for each step. MCP transforms AI models from conversation assistants into autonomous agents that can take real actions on external systems.

For the data lakehouse, MCP means that AI models can be given direct, autonomous access to governed enterprise data — discovering datasets, understanding their business meaning, formulating SQL queries, and returning business-contextualized answers — all without a human writing SQL or navigating the data catalog manually.

Dremio's MCP Tool Set

Dremio's MCP server exposes the AI Semantic Layer as the following MCP tools:

  • list_datasets: Returns all datasets accessible to the agent principal, with their business names, descriptions, and domain tags. The AI model reads descriptions to identify relevant datasets for the user's question.
  • get_dataset_schema: Returns the full schema of a specified dataset — column names, types, and AI-readable descriptions of what each column represents. Helps the AI model understand how to write correct SQL.
  • get_metric_definitions: Returns formal metric definitions — the exact SQL formula, valid dimensions, and business description for named business metrics (LTV, Churn Rate, Conversion Rate).
  • execute_sql: Executes a SQL query against Dremio's engine and returns results. The AI model formulates the SQL based on schema knowledge and the user's question.
  • get_query_status: Polls the status of a running query — enabling asynchronous execution of long-running analytical queries.
Dremio MCP Tool Architecture diagram
Figure 1: Dremio MCP tool set — the AI agent toolkit for autonomous lakehouse data access.

MCP in Multi-Agent Architectures

MCP's most powerful application is in multi-agent architectures where specialized agents collaborate:

  • Orchestrator agent: Receives user question, decomposes into sub-tasks, delegates to specialist agents
  • Data analyst agent: Uses Dremio MCP tools to retrieve data and perform SQL analysis
  • ML inference agent: Calls a feature store API and a model serving endpoint to generate ML predictions
  • Report agent: Combines data analysis and ML predictions into a formatted business report

Each agent uses MCP to access its specialized external systems. The orchestrator coordinates them without needing to know the details of each system's API — MCP's standardized tool interface provides the abstraction layer that makes multi-agent orchestration tractable.

Multi-Agent MCP Lakehouse Architecture diagram
Figure 2: Multi-agent MCP architecture — orchestrator delegating to data, ML, and reporting agents.

Summary

The Model Context Protocol is the infrastructure standard that makes the Agentic Lakehouse possible — providing a standardized, open interface for AI models to autonomously access enterprise data platforms. Dremio's MCP server, exposing the AI Semantic Layer as structured, AI-interpretable tools, is the practical implementation of this vision: connecting the world's most capable AI language models to the world's most sophisticated open lakehouse platform — with full governance, access control, and audit trail — for a new era of autonomous enterprise analytics. Every organization investing in the open data lakehouse today is laying the foundation for the agentic analytics capabilities that MCP and AI agents will deliver tomorrow.