An open standard by Anthropic for enabling AI agents to communicate, collaborate, and integrate with the world around them.
Model Context Protocol (MCP) was introduced in 2024 by Anthropic as an open standard that helps multiple AI agents work together efficiently. It enables:
- Structured communication between agents and tools
- Context sharing across sessions and systems
- Seamless integration with external tools and data sources
Before MCP connecting models to new data source requires custom implementation, which can get expensive.
After MCP agent collaborate in real-time using shared memory, enabling seamless interaction across tools like slack, notion, and Google Drive through unified protocol
| Before MCP | After MCP | |
|---|---|---|
| Integration | Each new data source required custom implementation | Unified protocol for all tools and sources |
| Cost | Expensive and time-consuming to scale | Reusable, standardized connections |
| Collaboration | Agents operated in silos | Agents collaborate in real-time using shared memory |
| Tools | Custom connectors per tool | Works out-of-the-box with Slack, Notion, Google Drive, and more |
MCP is built around three core components: Host, Client, and Server.
The Host is an LLM application (e.g., Claude Desktop) that provides the environment where the AI system runs. It manages interactions between the Client and Server — think of it as the home where the AI lives and operates.
The Client is a lightweight process or module that lives inside the Host. Its responsibilities include:
- Interpreting instructions from the AI agent
- Sending those instructions to the appropriate MCP Server
- Receiving results and relaying them back to the agent
The Server is where all the actual tools and data sources live — it's the action centre. It can be:
- A local app running on your computer
- A remote server hosted in the cloud
It houses predefined tools and services such as APIs, file access systems, and code execution environments.
MCP also handles turn-taking and message threading, ensuring that even when agents are spread across different systems — such as email, Slack, or custom tools — they remain in sync and can hand off context cleanly.
As AI becomes more collaborative and tool-dependent, MCP is setting the standard for how intelligent systems communicate, integrate, and scale. By providing a shared protocol, MCP reduces friction, lowers development costs, and unlocks the full potential of multi-agent AI workflows.