Manus is a large model AI Agent + MCP, so what is the Model Context Protocol (MCP)?

This requires a way to integrate various tools and assistants together. MCP is such a solution, which enables AI to better understand context, remember previous conversations, and call different tools when needed.

Imagine if your mobile phone, computer and headphones can be seamlessly connected with just a USB-C cable, how convenient life will become? Now, this concept is being transplanted into the field of artificial intelligence - MCP ( Model Context Protocol ) is the messenger carrying this vision.

Simply put, MCP is like the "brain center" of AI, helping it coordinate various capabilities and turning a model that originally only worked alone into a collaborative team that can handle complex tasks. Today's article introduces this recently popular technology to friends: MCP .

What is the Model Context Protocol

MCP (Model Context Protocol) is an open protocol that emerged in the era of large models and aims to standardize the way applications provide context (data) to large language models (LLMs) .

The Model Context Protocol (MCP) is an open standard launched by Anthropic that aims to solve the problem of connecting LLM applications to data sources through a unified client-server architecture. It supports access to local resources (such as databases, files) and remote resources (such as Slack, GitHub API) through the same protocol without the need for custom integration.

MCP exposes tools and interaction templates, with built-in security to ensure resources are fully controlled by the server.

Currently, MCP supports local operation, and in the future, enterprise-level certified remote support will be introduced to achieve secure sharing among teams. Through the desktop application (provided by Anthropic), developers can integrate MCP in a short time, quickly connect the large model Claude to multiple data sources, and promote the standardized development of AI integration.

MCP Architecture

MCP follows a client-server architecture, which includes the following core concepts:

MCP Hosts : LLM applications that initiate requests (such as Claude Desktop, IDE, or AI tools).

MCP Clients : Maintain a 1:1 connection with the MCP server inside the host program.

MCP Servers : Provide context, tools, and prompt information to the MCP client.

Local Resources : Resources on the local computer that are securely accessible to the MCP server (such as files and databases).

Remote Resources : Remote resources that the MCP server can connect to (e.g. via an API).

As shown below:

MCP client

MCP client acts as a bridge between LLM and MCP server. The workflow of MCP client is as follows:

• The MCP client first obtains a list of available tools from the MCP server .

• The user's query is sent to the LLM together with the tool description through function calling  .

• The LLM decides whether and which tools to use .

• If a tool is required, the MCP client will execute the corresponding tool call through the MCP server .

• The results of the tool invocation are sent back to the LLM.

• LLM  generates a natural .

• Finally, the response is displayed to the user .

We can find the client programs that currently support the MCP protocol in Example Clients.

Currently using Claude Desktop as MCP client

Download address: https://claude.ai/download .

MCP Server

The MCP server  is a key component in the MCP architecture. It can provide three main types of functions:

1. Resources : File-like data that can be read by clients, such as API responses or file contents.

2. Tools : Functions that can be called by LLM (user approval required).

3. Prompts : Pre-written templates to help users complete specific tasks.

These features enable the MCP server to provide rich contextual information and operational capabilities to AI applications, thereby enhancing the practicality and flexibility of LLM.

There are many MCP servers implemented by the community in the MCP Servers Repository and Awesome MCP Servers repos.

For example, this is a  PostgreSQL MCP Server tool that enables large models to answer questions based on data in PostgreSQL .

For example, in the following problem, the big model Claude does not know the table structure in the database at first, so he first sends a request to determine the corresponding fields in the orders table and the users table respectively, and then performs a join query on the two tables.

MCP official website: https://modelcontextprotocol.io

MCP GitHub: https://github.com/modelcontextprotocol

Native MCP service support: Quickly achieve localized data connection through the Claude desktop application, the application installation address is: https://claude.ai/download.

Open source service code library: Contains pre-built implementations of popular systems such as Google Drive, Slack, GitHub, etc., which are easy to deploy and test directly.

https://github.com/modelcontextprotocol/servers.

MCP and API

Why choose MCP instead of traditional API?

If you use APIs to connect large models to external tools, developers need to write independent code for each API, including document parsing, authentication methods, error handling, and subsequent maintenance, which is time-consuming and labor-intensive.

MCP is more like a "master key":

Single protocol : Connect to MCP once and you can connect to multiple tools and services without having to worry about each API separately.

Dynamic discovery : AI models can automatically identify and interact with available tools without being hard-coded in advance.

Bidirectional communication : Similar to WebSocket, MCP supports real-time, bidirectional data flow, allowing AI to both obtain information and trigger actions.

Benefits of two-way communication:  

Pulling data : AI can query a server, for example to check your calendar schedule.  

Trigger actions : AI can directly take actions, such as rescheduling a meeting or sending an email.

In contrast, traditional APIs are more like one-way "conversations" and lack the flexibility and real-time nature of MCP.

The differences in MCP and API features are shown in the following figure:

Importance of MCP

1. Implement modular and scalable AI systems

One of the biggest challenges facing AI is designing systems that are both flexible and scalable. MCP helps break down monolithic AI architectures into modular components. By separating models, contexts, and protocols , developers can: 

• Replace different AI models without disrupting the entire system.

• Dynamically introduce new contexts (e.g., adapt NLP models to new languages ​​or industries).

• Define a powerful protocol for AI model orchestration.

2. Solve data engineering challenges

For data engineers, MCP provides a framework for efficiently working with data pipelines .  

• Model : defines how data is structured and transformed.

• Context : Handles runtime parameters, environment setup, and version control.

• Protocols : Manage the movement of data between storage layers, processing frameworks, and machine learning workflows.

3. Enhance software design patterns

MCP complies with well-known software architecture principles, such as:

• Model-View-Controller (MVC) : MCP extends the logic of MVC by introducing Context as an explicit component that dynamically affects the model.  

• Event-driven architecture : MCP supports real-time context updates based on event streams, making it suitable for IoT, financial transactions, and recommendation engines.  

• Microservices Communication : The protocols in MCP ensure robust communication in microservices-driven applications.  

4. Building Adaptive AI Agents

In multi-agent AI systems, MCP provides a structured approach  for agent interactions :

• Model : Defines agent decisions.

• Background : Tracking environmental changes.

• Protocol : Establish agent-to-agent and agent-to-human communications.

MCP is more than a simple protocol, more than an architectural pattern, it is a powerful mindset shift when designing AI-driven applications and distributed systems. By effectively separating models, contexts, and protocols, organizations can build adaptive, scalable, and maintainable software solutions.

It is the "new foundation" for AI and tool communication, providing a unified and standard method for AI to flexibly connect to external data and tools. Unlike the previous API that required manual settings, MCP is more like an intelligent framework that allows AI to better understand the context and have stronger interactive capabilities.

As AI continues to merge with enterprise systems, understanding MCP will be critical for developers, engineers, and architects looking to future-proof their applications.