The core technology behind Manus: How to simplify AI integration through MCP

Written by

Silas Grey

Updated on:July-13th-2025

Model Context Protocol (MCP) is a new open protocol that aims to standardize how applications provide context to large language models (LLMs). MCP simplifies the integration of AI systems with external tools and data sources through a unified interface. Compared with traditional APIs, MCP provides a more efficient, flexible and dynamic communication method. This article will explore the core technology, architecture and advantages of MCP in practical applications.

What is MCP?

Model Context Protocol (MCP) is a standardized protocol for connecting AI agents with various external tools and data sources. You can think of it as the USB-C interface in the AI field - it provides a unified way to connect AI systems.

The core idea of MCP is to simplify the interaction between AI models and data, tools and services. Just as USB-C simplifies the connection between devices and computers, MCP enables AI systems to communicate more easily with external resources through standardized protocols.

Why choose MCP over traditional APIs?

Traditional API integration methods usually require writing custom integration code for each service or data source. This means that developers need to deal with different documentation, authentication methods, error handling, and maintenance. MCP greatly simplifies this process through a unified protocol.

Limitations of Traditional APIs

Traditional APIs are like doors with their own unique keys and rules. Each API requires separate integration code, which not only increases development complexity but also limits the flexibility and scalability of the system.

Advantages of MCP

MCP significantly outperforms traditional APIs through the following features:

• Single protocol : MCP acts as a standardized “connector” that enables access to multiple tools and services with just one integration.
• Dynamic Discovery : MCP allows AI models to dynamically discover and interact with it without hard-coding each integration.
• Two-way communication : MCP supports persistent, real-time, two-way communication, similar to WebSockets. AI models can not only retrieve information, but also trigger actions dynamically.

MCP architecture and working principle

MCP uses a simple client-server architecture:

• MCP Host : Applications that need to access external data or tools (such as Claude Desktop or AI-driven IDEs).
• MCP Client : A client that maintains a one-to-one connection with an MCP server.
• MCP Server : A lightweight server that exposes specific functionality through MCP, connecting to local or remote data sources.
• Local data source : A file, database, or service that is securely accessed by the MCP server.
• Remote services : External Internet APIs or services accessed through the MCP server.

The core role of MCP is to coordinate the flow of data and instructions between AI models and tools without handling complex logic.

Practical application scenarios of MCP

1. Travel Planning Assistant

With traditional APIs, you would need to write code for Google Calendar, email, and airline booking APIs separately. With MCP, your AI assistant can seamlessly check your calendar, book your flight, and send your confirmation emails through the MCP server without having to write custom integrations for each tool.

2. Advanced IDE (Intelligent Code Editor)

Traditional APIs require manual integration of file systems, version control, package managers, and documentation. MCP connects these tools through a single protocol, enabling IDEs to provide richer context awareness and more powerful suggestions.

3. Complex data analysis

Traditional APIs require manual management of connections for each database and data visualization tool. MCP enables the AI analytics platform to autonomously discover and interact with multiple databases, visualization, and simulation tools through a unified MCP layer.

Advantages of MCP

• Simplified development : Write once, integrate many times without having to rewrite custom code for each integration.
• Flexibility : No complex reconfiguration required when switching AI models or tools.
• Real-time responsiveness : MCP connections remain active, enabling real-time context updates and interactions.
• Security and Compliance : Built-in access controls and standardized security practices.
• Scalability : Easily add new capabilities as the AI ecosystem grows.

When to use the legacy API?

Although MCP performs well in many scenarios, there are some situations where the traditional API may be more appropriate. For example, when precise, predictable interactions and tight control are required, the traditional API may be more appropriate.

Applicable scenarios of traditional API

• Requires fine-grained control and highly specific functionality.
• Tight coupling is required for optimal performance.
• Requires maximum predictability and minimum contextual autonomy.

How do I get started with MCP?

1. Define capabilities : Clarify what functions your MCP server will provide.
2. Implement the MCP layer : follow the standardized MCP protocol specifications.
3. Choose the transport : Decide whether to use local (stdio) or remote (Server-Sent Events/WebSockets) transport.
4. Create resources/tools : Develop or connect to specific data sources and services that the MCP will expose.
5. Set up the client : Establish a secure and stable connection between the MCP server and the client.

Summarize

MCP provides a unified and standardized way to integrate AI agents and models with external data and tools. It is more than just an API, but a powerful connection framework that supports intelligent, dynamic and context-rich AI applications.