AI and the Bridge to the Real World: MCP Protocol Analysis

Introduction

Recently, the field of artificial intelligence has welcomed an important technological breakthrough—the Model Context Protocol (, abbreviated as MCP ). This open-source protocol developed by Anthropic aims to address the fragmentation issue of AI models interacting with external tools and data. MCP is hailed as "the USB-C of AI," providing a unified standard interface for the interaction of AI agents with the real world.

Core Concepts of MC

MCP is essentially a client-server architecture communication protocol. It consists of three main components:

1. MCP host: user interaction applications, such as Claude Desktop, etc.
2. MCP Client: Embedded in the host, responsible for establishing connection and communication with the server.
3. MCP Server: A lightweight program that provides specific functions and connects to local or remote data sources.

MCP implements functionality through three "primitives":

1. 工具(Tools): Executable functions used to accomplish specific tasks.
2. 资源(Resources): structured data, used as contextual input.
3. Prompts (: predefined instruction templates that guide AI in using tools and resources.

![Understanding MCP in One Article: The Standardization Revolution of AI Intelligent Body Tool Interaction])https://img-cdn.gateio.im/webp-social/moments-6c38b4cabce95547f0239e4603557900.webp(

Advantages of MC

1. Real-time data access: AI can query the latest data in seconds.
2. Security and Control: Direct access to data without intermediate storage, reliable permission management.
3. Low computational load: No need to embed vectors, significantly reducing computational costs.
4. Flexibility and Scalability: Significantly simplify the integration of models and tools.
5. Interoperability: A MCP server can be shared by multiple AI models.
6. Vendor Flexibility: Switching AI models without reconstructing the infrastructure.
7. Autonomous agent support: Supports AI dynamic access tools to perform complex tasks.

![Understanding MCP in One Article: The Standardization Revolution of AI Intelligent Body Tool Interaction])https://img-cdn.gateio.im/webp-social/moments-278e2ffd1d529c551cfe3d9c4e11f5a6.webp(

Application Scenarios

MCP demonstrates great potential in multiple fields:

1. Development and Productivity: Code debugging, document search, task automation, etc.
2. Creativity and Design: 3D modeling, design task assistance, etc.
3. Data and Communication: database queries, team collaboration, web scraping, etc.
4. Education and Healthcare: Course planning, medical diagnostic assistance, etc.
5. Blockchain and Finance: Blockchain transaction inquiry, financial analysis, etc.

![Understanding MCP in One Article: The Standardization Revolution of AI Intelligent Tool Interaction])https://img-cdn.gateio.im/webp-social/moments-f8ffe35d3d68ff0e4f35986e45810b63.webp(

MCP Ecosystem Status

By March 2025, the MCP ecosystem has taken shape.

• The number of servers exceeds 2000, covering multiple categories including databases, tools, and creativity.
• Over 300 GitHub projects participated in development.
• The main market platform mcp.so has recorded 1584 servers, with over 100,000 monthly active users.

![A Comprehensive Understanding of MCP: The Standardized Revolution of AI Intelligent Tool Interaction])https://img-cdn.gateio.im/webp-social/moments-24022832efba6b8908c70fabfcdef78b.webp(

Challenges and Limitations

Despite the bright prospects, MCP still faces some challenges:

1. Technical complexity: High development difficulty, with limitations in deployment and debugging.
2. The quality of the ecosystem is uneven: some servers have stability issues or missing documentation.
3. Insufficient discoverability: Lack of a mature dynamic discovery mechanism.
4. Applicability in Production Environment: Invocation errors may occur in complex tasks.
5. Competitive Pressure: Facing competition from proprietary solutions of companies like OpenAI.

Future Outlook

The development direction of MCP may include:

1. Protocol simplification and optimization to enhance developer friendliness.
2. Establish a marketplace similar to npm to improve server discovery and installation experience.
3. Expand to more business scenarios, such as customer support, marketing, and other fields.
4. Enhance the quality and scale of the ecosystem through community incentives.

2025 will be a critical juncture for the development of MCP. If the current technological and ecological challenges can be addressed, MCP is expected to become the infrastructure for the AI agent ecosystem, promoting a closer integration of AI with the real world.

![Understanding MCP in One Article: The Standardization Revolution of AI Intelligent Tool Interaction])https://img-cdn.gateio.im/webp-social/moments-9e1a876fd70133831993c3bbc0f4d96d.webp(