MCP (Model Context Protocol) is an open protocol that lets AI models securely connect to external tools, data sources, and applications through a standardized interface.

Think of it as "USB-C for AI applications": instead of building a custom integration for every database, API, or service, an AI assistant can use MCP to interact with many systems through a common protocol.

Why MCP exists

Without MCP:

• Every AI app needs custom code for every tool.
• Integrations are difficult to maintain.
• Switching models or providers often requires rebuilding connections.

With MCP:

• Tools expose a standard MCP interface.
• Any MCP-compatible AI client can use them.
• Integrations become reusable across models and applications.

How it works

MCP typically involves three components:

• Host
  • The AI application (e.g., an AI coding assistant, desktop app, or chatbot).
• MCP Client
  • Embedded in the host.
  • Communicates with MCP servers.
• MCP Server
  • Provides capabilities such as:
    • Database access
    • File access
    • Git repositories
    • APIs
    • Business systems
    • Custom tools

AI App (Host)
      |
      v
 MCP Client
      |
      v
 MCP Server
      |
      +--> Files
      +--> Databases
      +--> APIs
      +--> External Tools

What MCP servers can provide

An MCP server can expose:

Tools – actions the model can perform

• Run SQL queries
• Create tickets
• Call APIs

Resources – data the model can read

• Documents
• Files
• Database records

Prompts – reusable prompt templates

• Workflows
• Task-specific instructions

Example

Suppose an AI assistant needs access to a company's customer database.

Instead of writing a custom integration:

• The company runs an MCP server.
• The server exposes a tool like:
• get_customer(id)
• The AI client discovers the tool automatically.

The model can request customer information through the MCP interface.

Benefits

• Standardized integrations
• Model-agnostic architecture
• Better tool discovery
• Easier maintenance
• Improved security controls
• Reusable ecosystem of connectors

MCP and AI coding tools

Many AI development environments and agents now support MCP servers to connect to:

• Git repositories
• Issue trackers
• Documentation systems
• Databases
• Cloud services

This allows the AI to work with real project context rather than only the text in the chat.

Who created it?

MCP was introduced by Anthropic and has since gained broad adoption across the AI ecosystem, with many AI tools, frameworks, and companies building MCP-compatible clients and servers.

In one sentence: MCP is a standardized protocol that enables AI models to securely discover and use external tools, data, and services.