Definition
The Model Context Protocol (MCP) is an open specification that standardizes how AI clients talk to external tools and data. A server implements MCP and exposes a set of tools (functions the client can call), resources (data the client can read), and prompts (named templates the client can use). A client — Claude Desktop, ChatGPT, Cursor, or any other MCP-compatible application — discovers what the server offers and calls it on behalf of the user. The protocol was introduced by Anthropic in late 2024 and saw broad adoption through 2025 and into 2026, as of Q1 2026.
Before MCP, every integration between an AI client and a data source was custom-built per pair. MCP turns that into a common interface, the way HTTP turned per-browser network protocols into one.
Why it matters
For a consumer-brand feedback team, MCP matters because it lets the feedback corpus live inside the tools people already use. A Product lead working in Claude Desktop can ask, "What are this month's top complaints on our new blender?" and the AI client calls an MCP server that has access to the brand's feedback data, runs the query, and returns a grounded answer. No export, no tab-switching, no copy-paste. The same works from ChatGPT in the browser or Cursor while a developer is scripting.
It matters structurally because it separates the intelligence layer from the interface layer. The brand owns the data and the agents. The user picks the AI client. Switching clients does not require migrating data. It also matters for governance: the server defines what tools are available, which records are visible, and who is allowed to call what — policy lives once, in the server, rather than copied across every client integration.
Example
A consumer electronics brand installs the Indellia MCP Server and points it at its Indellia tenant. A Product Manager opens Claude Desktop and asks, "What themes are spiking this week on our new soundbar across Amazon, Walmart, and Best Buy?" Claude discovers the tools on the Indellia MCP Server, calls the theme and anomaly tools, and returns a ranked list with record counts and a link back into Indellia for the underlying reviews. The same night, a CX lead in ChatGPT asks the same question and gets the same answer. A developer in Cursor calls the same MCP Server to pull a filtered corpus into a Jupyter notebook for deeper analysis. The Indellia MCP Server is shipped.