AI Tech NewsYour portal to artificial intelligence
Home / What is MCP? The USB-C of the AI World: A Universal Standard for AI Assistants to Read Data and Connect Tools
Standards & Collaboration

What is MCP? The USB-C of the AI World: A Universal Standard for AI Assistants to Read Data and Connect Tools

If you have ever wanted your AI assistant to directly query your Google Drive, read Notion notes, or call an API to get work done, you've probably felt this pain: every AI tool has its own integration method, and developers have to write integration code separately for each combination.

The emergence of MCP is designed to solve this exact problem.

If an AI model were a laptop, then MCP would be like USB-C—allowing all tools and data sources to plug in through the same port.

One-Sentence Summary

MCP (Model Context Protocol) is an open protocol proposed by Anthropic in late 2024 that standardizes how AI assistants communicate with external data sources (like Google Drive, GitHub) and tools (like executing code, calling APIs).

What Problems Does It Solve?

Before MCP, every AI tool had its own integration method:

  • ChatGPT's Plugins used one approach.
  • Claude's Tool Use used another.
  • LangChain's Tools used yet another.

This trapped developers in a dilemma of "writing integration once for every single combination."

MCP defines a standard communication protocol, so that:

  • Any AI model supporting MCP can use any tool supporting MCP.
  • Tool developers only need to write it once for all AI models to use.

Core Features

1. Standardized Tool Interfaces

MCP defines three core capabilities:

  • Resources: Allows AI to read external data (e.g., documents, databases, API responses).
  • Tools: Allows AI to execute specific actions (e.g., writing files, calling APIs, executing code).
  • Prompts: Allows servers to provide predefined prompt templates that AI can call directly.

2. Open Standard, Not Anthropic Exclusive

Although proposed by Anthropic, MCP is an open specification. Currently, major players like OpenAI, Google DeepMind, Cursor, and Zed have all announced support.

3. Local and Cloud Support

MCP servers can run locally (to protect privacy) or be deployed in the cloud (to increase accessibility).

Who Is It For?

  • AI Tool Developers: You only need to write an MCP Server once to make your service available to all AI tools that support MCP.
  • Enterprise IT Teams: You can wrap internal systems (like ERP, CRM, document repositories) into an MCP Server, allowing enterprise AI assistants direct access.

Our Observations

MCP is rapidly becoming the de facto standard for AI tool integration. In just a few months, thousands of MCP Servers have been built, covering integrations from GitHub, Slack, and Google Workspace to various databases. Its arrival drastically lowers the barrier to integrating AI with existing systems.

Sources