Model Context Protocol (MCP) is an open standard that defines how AI models connect to external tools and data sources. Instead of every AI provider building custom integrations with every app, MCP creates a common interface: if an app supports MCP, any AI model that supports MCP can connect to it. It's the difference between plug-and-play tool integration and one-off custom connections.

The problem MCP solves

Before MCP, connecting an AI model to your tools required custom integration work for each combination. Want Claude to access your Notion workspace? Custom integration. Want GPT-5.5 to read your Google Calendar? Different custom integration. Want either model to send emails? Two separate integrations.

This meant AI assistants had inconsistent and limited tool access depending on who built what integration for which model.

MCP — created by Anthropic and adopted by the broader AI ecosystem — creates a standard. An app that exposes an MCP server can be connected to by any AI model that supports the MCP client. Claude, GPT-5.5, and others can all connect to the same MCP-compatible tools without separate integrations.

Who has adopted MCP

MCP was initially released by Anthropic in late 2025. By June 2026:

  • Anthropic's Claude Code uses MCP natively

  • OpenAI has added MCP support to Codex CLI and GPT-5.5 API

  • Devin Desktop (formerly Windsurf) includes ACP (Agent Client Protocol), a related standard adopted by JetBrains, Google, GitHub, and 25+ other tools

  • Major integrations available include: GitHub, Google Drive, Notion, Slack, Gmail, databases, file systems

What MCP-connected AI can do

When a model connects to an MCP server, it gets access to the tools and data that server exposes. Practical examples:

GitHub MCP server: the AI can create issues, read pull requests, check build status, and review code directly — without copy-pasting from your browser.

Notion MCP server: the AI can read your Notion pages, create new entries, update databases, and search your workspace.

Database MCP servers: the AI can query your database, read schema information, and run analysis on your data — without you manually exporting to CSV.

File system servers: the AI can read and write files on your computer or network storage.

MCP in practice

The most common MCP use case in 2026 is Claude Code and Codex CLI reading project context — configuration files, READMEs, existing code — automatically when you run them in a project directory. The tool server exposes the local file system to the AI, so you don't have to manually paste in context.

More advanced setups connect agents to multiple tools simultaneously: an AI that can read your database, check your calendar, and send Slack messages as part of a single workflow. This is where agentic AI starts to replace what would have been multi-step manual work.

Developer use

For developers building AI-powered applications, MCP is increasingly the standard way to extend AI capabilities with external tools. If you're building on Claude, GPT-5.5, or other models that support MCP, connecting to external services through MCP servers is simpler than building custom function calling implementations for each service.

The MCP ecosystem has grown substantially since its introduction. Most major developer tools now have MCP servers available — either officially or through community implementations.

The simplest mental model

MCP is the USB standard for AI tools. Before USB, every device needed a different cable. After USB, the same port works for keyboards, mice, storage, and more. MCP does the same thing for AI-to-tool connections: one standard, many compatible tools.

If a tool supports MCP, any MCP-compatible AI model can connect to it. That's the whole idea.