Model Context Protocol: How to use LLMs to query your analytics data

Most analytics tools make you choose: either get quick answers with surface-level insights, or dig deep but wait hours, maybe days, for meaningful analysis.

Model Context Protocol (MCP) eliminates that trade-off. Instead of clicking through dashboards or relying on the data team to write SQL queries, you can ask your analytics data complex questions in natural language and get deep, actionable insights instantly.

For product and marketing teams who need both speed and substance from their data, here's how MCP turns analytics conversations into faster, smarter decisions.

What is a Model Context Protocol (MCP)?

A Model Context Protocol (MCP) is an open standard that allows applications and large language models (LLMs) to communicate using structured context. Instead of treating an AI model like a black box, MCP creates a controlled framework where models can request and receive data securely, consistently, and in a digestible format.

For example, instead of manually coding integrations between an LLM and a data warehouse, MCP provides a universal “handshake” that makes context exchange reliable and reusable.