Agent and MCP: A new paradigm for intelligent assistants

With the rapid development of artificial intelligence technology, how AI models can interact efficiently with the real world has become a key issue. The traditional Function Calling method has problems such as inconsistent interfaces and poor scalability, and the emergence of Model Context Protocol (MCP) provides an elegant solution to these problems. This article will explore the working principle, application scenarios, and ecosystem of MCP in depth to help readers fully understand this revolutionary technology paradigm.

MCP: Let AI models communicate with the real world

Model Context Protocol (MCP) is an open communication protocol that acts like a "universal converter" that allows AI models to easily use a variety of real-world tools and resources. Through standardized interface definitions and flexible extension mechanisms, MCP is redefining how AI interacts with the outside world.

The core design of MCP can be summarized in three sentences:

1. 1. Through a unified interface and standardized tool registration mechanism, seamless connection between AI models and external tools is achieved
2. 2. Intelligent resource management and secure data transmission mechanism ensures efficient and secure system operation
3. 3. Context-aware intelligent scheduling provides smooth user interaction experience and accurate tool calling

Real world ?MCP protocol ?AI model ?Large language model Standardized interface Tool registration Resource management Context processing File system External API Database Various services

To better understand how MCP works, let's look at its specific workflow:

MCP Workflow

Tool ?️MCP Server ?️LLM ?MCP Client ?User ?Tool ?️MCP Server ?️LLM ?MCP Client ?User ?Tool discovery phase Query processing phase Tool call phase alt[need to use tool] Result processing phase 1. Get the list of available tools Return the tool list and description Send query request 2. Forward query and tool description 3. Intelligent analysis requirements 4. Initiate tool call 5. Perform specific operations Call the target tool Return execution results Pass execution results 6. Provide result data 7. Generate reply content Display the final result

Workflow Description:

1. 1.  Tool discovery : MCP Client obtains a list of available tools and their functional descriptions
2. 2.  Query processing : Client converts the tool into a standard format and sends it to LLM

   {
     "name" : "search_weather" ,
   "description" : "Get the weather information of the specified city" ,
   "parameters" : {
       "city" : "city name" ,
       "days" : "Day forecast (1-7 days)"
   }
   }

3. 3.  Decision Analysis : LLM selects the appropriate tool based on user needs
4. 4.  Tool call : execute tool call and obtain results through MCP Server
5. 5.  Result processing : return the tool execution results to LLM for analysis
6. 6.  Response Generation : LLM generates natural language responses
7. 7.  Result display : Display results to users and maintain conversation context

Application scenario: Japan travel planning assistant

Let's use a travel planning assistant to demonstrate how Agent and MCP work together.

System composition

MCP Toolset ?️ Attractions/Transportation/Restaurants Itinerary Document Route/Budget Complete Plan ?User ?Smart Assistant ?Search Tool ?File Tool ?Analysis Tool ?

Workflow

1. 1.  Demand understanding : Collect user travel needs (time, budget, preferences)
2. 2.  Information acquisition : search for information on attractions, accommodation, transportation, food, etc.
3. 3.  Program planning : design itinerary, arrange route, estimate cost
4. 4.  Document generation : make itinerary manuals, transportation guides, and precautions

Required MCP Services

Through this example of a travel planning assistant, we can see how MCP integrates multiple services to solve complex problems. So, compared with the traditional Function Tool solution, what are the advantages of MCP? Let's make a comparison:

Function Tool vs. MCP

From the above comparison, we can see that MCP has obvious advantages in functionality and scalability. It is precisely because of these advantages that MCP has attracted many developers and enterprises to join the ecological construction. Let's take a look at the current MCP service ecology:

MCP Service Ecosystem

The MCP service ecosystem is growing rapidly. You can find more MCP services at the following addresses:

• • Community MCP servers: https://glama.ai/mcp/servers
• • Official MCP servers: https://github.com/modelcontextprotocol/servers

Here are some typical MCP service examples:

Summary and Outlook

Through the analysis of MCP's working principle, application scenarios, and service ecology, we can see that:

1. 1.Technological  advantages

• • Unified communication protocols simplify the integration of AI models with external tools
• • Standardized interface definitions improve tool reusability and interoperability
• • Flexible expansion mechanism supports rapid adaptation to new application scenarios

• 2.  Development Trend
• • The ecosystem will be further expanded to cover more professional fields
• • Enterprise-level applications will become an important force driving the development of MCP
• • Community-driven innovation will lead to more high-quality tools and services

The emergence of MCP marks a new stage in AI application development. It not only solves the pain points of the current AI model's interaction with the outside world, but also provides a reliable technical foundation for the development of future AI applications.