The problem MCP solves
AI agents like Claude, ChatGPT, and Gemini are powerful — but they're isolated. They can answer general questions about the web, but they can't interact with your specific website. They can't browse your product catalogue, check your availability, or search your content in real time.
Before MCP, every website that wanted AI agents to access it had to build a custom integration for each AI platform separately. That's expensive, fragile, and doesn't scale.
How MCP works
The Model Context Protocol is an open standard — originally created by Anthropic and now governed by the AI Alliance / Linux Foundation — that defines how AI agents discover and call capabilities on external services. It works like this:
- A service exposes an MCP endpoint — a URL that speaks the MCP protocol
- An AI agent connects to that endpoint and asks: "What capabilities do you have?"
- The endpoint responds with a list of available capabilities (e.g. "search products", "get page content")
- The agent calls those capabilities as needed to complete the user's request
It's a clean request-response protocol over HTTP. No webhooks, no streaming data feeds, no complex auth flows. Just a standardised way for AI to interact with your services — regardless of which AI the user is using.
MCP discovery: how agents find your endpoint
For an agent to use your MCP endpoint, it first needs to know it exists. MCP discovery works via a well-known URI — a file at /.well-known/mcp.json on your domain — that tells agents where your endpoint is and what it supports.
This discovery mechanism is based on an IETF Internet-Draft (draft-serra-mcp-discovery-uri) and MCP community proposals. LiftMCP publishes this file on your behalf the moment you verify your domain — agents that support MCP discovery will find your endpoint automatically.
The bigger picture
From SEO to AEO — Agent Engine Optimisation
Search engine optimisation taught businesses to structure their content for crawlers. The next shift is structuring your services for agents. When someone asks Claude "find me a hotel in Edinburgh with a pool", the agent needs a live, structured way to search hotel websites — not just recall training data.
Websites without MCP are invisible to this new class of interaction. Websites with MCP become discoverable, queryable, and actionable — directly from within the AI conversation. That's AEO: making sure your site is ready for the agent-driven web, not just the search-driven one.
Is MCP production-ready?
MCP is being adopted rapidly across the AI ecosystem. Claude (Anthropic), Cursor, and a growing number of AI tools already support MCP clients. The protocol has moved from Anthropic's internal tooling to community governance under the AI Alliance, with active standardisation work ongoing.
The discovery mechanism is still evolving — but the direction is clear, and early movers will be best positioned as agent-driven traffic grows. Your MCP endpoint today means your site is ready when agents become the primary way users interact with the web.
Where LiftMCP comes in
MCP infrastructure, without the engineering.
You shouldn't need to build your own MCP server, manage the discovery file, or handle protocol updates. LiftMCP handles all of that — hosting, security, discovery, and capability management. You add your website, choose what to expose, and you're live.