The Model Context Protocol (MCP) is an open standard that lets AI applications — Claude, ChatGPT, Cursor, Codex, and other agents — connect to external data sources and tools through a single uniform interface. Instead of building a custom integration for every AI client, a company runs one MCP server, and any compatible agent can discover its tools, call them, and act on live data. Anthropic released MCP in November 2024; since December 2025 the protocol has been governed by the Agentic AI Foundation under the Linux Foundation and is supported across every major AI platform.
Key takeaways
- MCP is often described as USB-C for AI: one open protocol replaces the N-times-M tangle of custom integrations between AI clients and data sources.
- An MCP server exposes three primitives over JSON-RPC — tools (actions an agent can call), resources (data it can read), and prompts (reusable templates).
- The standard is vendor-neutral. OpenAI, Google, and Microsoft all ship MCP support, and the project reported 97 million monthly SDK downloads and more than 10,000 active servers when it joined the Linux Foundation in December 2025.
- For brands, MCP is the plumbing of agentic commerce: it turns a site an AI can merely read into a system an AI can query — live inventory, live pricing, live analytics.
- GEOly runs a hosted MCP server with 62 GEO tools, so marketers can pull AIGVR scores, Share of Model, and citation data into Claude, Cursor, or Codex without opening a dashboard.
How MCP works
MCP defines three roles. The host is the AI application you talk to — Claude Desktop, ChatGPT, Cursor. The host runs a client for each connection it holds. The server is the piece that exposes capabilities: a database, a SaaS platform, a file system, an e-commerce backend.
When a session starts, the client asks the server what it offers and gets back a machine-readable catalog: tool names, descriptions, input schemas. From that point the model can decide mid-conversation that a question needs external data, pick the right tool, and call it. Messages travel as JSON-RPC, over stdio for local servers or streamable HTTP for hosted ones, with OAuth typically handling identity. The full specification lives at modelcontextprotocol.io.
A concrete run: you ask your agent which domains cite your main competitor most often. The model scans the tool catalog, selects get_citation_overview, sends the brand as a parameter, receives structured JSON, and writes its answer from real numbers instead of guesses. Nothing was retrained. The data was live at the moment of the call, and the server — not the model — decided what this user was allowed to see.
