MCP and AI agents

What is MCP, and why every marketplace will need one

Mira team30 September 20263 min read

MCP (Model Context Protocol) is an open standard that lets AI assistants connect to outside tools and data. For a marketplace, an MCP server is the door through which ChatGPT, Claude or Gemini can search live listings and show them to a user mid-conversation. Without one, the assistant answers from what it already knows, and your inventory stays outside the conversation.

What is MCP, in plain terms?

Before MCP, every AI product needed its own custom integration for every data source. MCP replaces that with one shared language. A business publishes an MCP server that describes what it can do, for example "search listings by price, location and features", and any assistant that speaks MCP can call it.

An MCP server is to AI assistants what a website is to browsers. It is the standard way to be reachable. A marketplace with an MCP server can be searched by any compatible assistant; a marketplace without one depends on whatever the assistant learned from the open web.

Who supports MCP today?

Anthropic released MCP in November 2024. OpenAI adopted it in March 2025, and in October 2025 launched apps in ChatGPT built on MCP. In December 2025 the protocol was donated to the Agentic AI Foundation under the Linux Foundation, co-founded by Anthropic, Block and OpenAI with support from Google, AWS and Microsoft.

Adoption has followed. At the time of the donation, Anthropic reported more than 97 million monthly SDK downloads and over 10,000 active public MCP servers. When competing AI companies agree on one protocol, it usually becomes the default.

Why does MCP matter for marketplaces specifically?

Marketplaces live on inventory that changes every hour. A listing published this morning may be sold by tonight. AI assistants cannot know this from training data or from crawling once a week. They need a live connection, and MCP is how they get it.

This is already visible. When OpenAI launched apps in ChatGPT, built on MCP, its own example was a property portal: a user talking about buying a home sees matching listings on an interactive map, without leaving ChatGPT. A report by OC&C Strategy Consultants names MCP as a standard way for marketplaces to expose product search to AI agents.

What should a marketplace expose through MCP?

Search over live listings, with the filters your users already rely on.
Listing details: photos, price, condition, location and seller information.
A clear link back to the listing on your site, so the lead lands with you.
Rules you control: which categories and listings appear.

The goal is to let the assistant use your search, not replace it. Your live listings and your brand stay the source of truth: the assistant becomes one more entrance to the same listings.

Is building an MCP server enough?

A server that returns raw fields is a start. The harder part is translation. Users ask assistants in full sentences, and an MCP server that only accepts exact field values will miss most of them. The marketplaces that benefit most will pair MCP with a layer that understands requests like "a reliable first car for my daughter" and turns them into the right searches.

Where Mira fits

Mira builds and hosts this connection for marketplaces: your MCP, connected to your live listings and packaged for the AI assistants you choose, from ChatGPT to Claude. You publish it under your name. It runs on the same engine as Mira on your site (widget), and your search stays as it is. See how Mira works for marketplaces.

Frequently asked questions

Is MCP only for ChatGPT?

No. MCP is an open standard supported by several assistants, including ChatGPT, Claude and Gemini. One MCP server can serve all of them.

Does MCP give AI companies our data?

MCP exposes only what you choose to expose, such as public listing data and search. Users still click through to your site to contact sellers.

How long does it take to set up?

It depends on your search setup. The date is agreed after a technical call with your team.

Curious how your users' questions perform on your search?

Let's talk