Without a common protocol, every AI app would need custom code for every data source or tool it wants to use, and every service would need to build a separate integration for each app. That doesnβt scale. MCP gives one standard: servers expose tools and resources in a uniform way; clients connect, discover whatβs available, and pass that to the LLM. So you build one MCP server (e.g. "company docs" or "flight search") and any MCP-compatible app β Cursor, Claude Desktop, your own agent β can use it without new integration code.
MCP conversation flow
search_flights(destination: Tokyo)Without a standard
- N integrations per appβ Every app (Cursor, Claude Desktop, your chatbot) would need its own code for files, DB, search, etc.
- N integrations per serverβ Every service that wants to be used by AI would need to build a separate plugin for each app.
- No standard shapeβ Tools and resources would look different everywhere; LLMs and apps can't assume one consistent format.
With MCP
- Integrate onceβ Build one MCP server; any MCP-compatible app can connect and use it.
- Discover at runtimeβ Client asks the server what tools and resources it has; no hardcoded list per integration.
- Same protocol everywhereβ Tools have name + parameters; resources have URIs. Same shape for every server.
Example: Real-world impact
Today: Cursor has a filesystem MCP server; Claude Desktop can use the same kind of server. If you build a "customer DB" MCP server, both can talk to it the same way. Without MCP, Cursor would need a Cursor-specific plugin and Claude would need a different one β and youβd maintain two code paths.