Discover how the Model Context Protocol (MCP) works, why OpenAI, Anthropic, Google, and Microsoft support it, and how it simplifies AI tool integration for developers.
Before MCP, every AI-to-tool connection was custom-built, ten apps talking to a hundred tools meant up to a thousand fragile, one-off integrations. MCP replaces that with one protocol: build theconnection once, and it works with any MCP-compatible AI tool.
One developer described spending two weeks building a custom CRM plugin, then replacing it with an MCP server built in about four hours, one that worked with every model in their stack afterward. That ratio is the whole business case
| Primitive | What it is | Who controls it |
|---|---|---|
| Tools | Functions the AI can call (create a file, query a database) | The model decides when |
| Resources | Data the AI can read (files, records, API responses) | The app or user decides |
| Prompts | Reusable templates for structured tasks | The user selects it |
Messages run on JSON-RPC 2.0. The real value: any MCP-compatible tool (Claude Code, Cursor, Windsurf, VS Code) talks to any MCP server without either side needing to know about the other in advance.
Not a niche standard a handful of teams are experimenting with:
"The most substantial changes we have made to the specification, probably since adding authorization." - Anthropic's David Soria Parra
Not fully backward compatible, if you maintain an MCP server, check the migration notes this week.
Does it replace REST APIs? No. MCP wraps existing APIs in a layer an AI model can discover and call correctly, it doesn't replace them for human or traditional service use.
MCP is closer to plumbing now than a trend: unglamorous, mostly invisible when it works, and increasingly assumed by default. If an AI model needs to touch a real tool or workflow, a custom integration is now the harder path.
Reflects the protocol's status as of July 28, 2026. Check the official specification for the current version before building against it.
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