The Technological Trend Amidst the AI Agent Boom

In the early hours of March 6, the Monica team announced that their new universal Agent outperformed OpenAI's "Deep Research" model on the GAIA Benchmark. Meanwhile, the latest demonstration by ManusAI has garnered significant attention. Recently, Microsoft unveiled its AI Agent toolchain based on Azure, while domestic AI companies like ZhiPu AI are advancing the development of universal Agents. JD.com is also exploring the application of AI Agents in e-commerce and logistics. These events reveal a common need: how to seamlessly connect AI with external tools and data to truly bridge the gap from "generation" to "action."

AI Agents are evolving from mere conversational tools into intelligent assistants capable of autonomous actions and solving complex problems. Behind this evolution, a seemingly inconspicuous yet crucial technology—Model Context Protocol (MCP)—is quietly emerging as the "super connector" for AI Agents.

I. What is MCP? Understanding its Necessity from Industry Pain Points

The core of AI Agents lies in "tool calling" and context awareness. For example, Microsoft's Azure AI Agent connects to enterprise data via predefined APIs to automate tasks, while JD.com leverages Agents to optimize supply chain scheduling. However, these solutions often rely on customized development, making them difficult to reuse across platforms. MCP was introduced to address this pain point.

Definition of MCP:
MCP is a communication protocol based on JSON-RPC 2.0, designed to provide a unified interface for large language models (LLMs) to access external resources. Drawing inspiration from the Language Server Protocol (LSP), MCP decouples AI from tools and data sources through a client-server architecture.

Technical Basis:

• Communication Protocol: JSON-RPC 2.0 supports lightweight request-response patterns and is widely used in distributed systems (e.g., the LSP implementation in VS Code).

• Existing Pathways: Similar to OpenAI's Function Calling and Google's Vertex AI tool integration, MCP further standardizes this process.

• Industry Demand: In 2025, AI Agents (proxy AI) are becoming the focus of the AI field. Microsoft's Q4 2024 financial report mentioned that 30% of AI Agent deployment costs are allocated to integrating external systems. According to Statista, the global AI market is projected to reach $190 billion by 2025, with AI Agent-related applications (such as automated workflows and intelligent assistants) accounting for a significant share of this growth.

  II. The Technical Core of MCP: Empowering AI Agents

  MCP's architecture is clear and modular, as follows:

  Components:

  • MCP Host: The platform running AI applications (e.g., Claude Desktop or Microsoft Copilot).

  • MCP Client: Embedded in the host, responsible for communicating with the server.

  • MCP Server: An independent program providing specific functions (e.g., file read/write, API calls).

  Interface Specifications (based on Anthropic's official documentation):

  • Tools: Define executable actions, such as "query database" or "call Slack API."

  • Prompts: Predefined templates to optimize interaction efficiency.

  • Resources: Returned data, such as file content or API responses.

  Communication Process:

  • Request: The client sends a JSON-RPC request (e.g., {"method": "getFile", "params": {"path": "/doc.txt"}}).

  • Response: The server returns the result (e.g., {"result": "file content"}).

  • Security: Access permissions are controlled through host authorization.

  Technical Implementation:

  • Protocol Stack: JSON-RPC is based on HTTP or WebSocket, making it easy to debug and extend.

  • Reference Case: Similar to LangChain's tool calling, MCP enhances universality through standardized interfaces.

  • Example: For a retail enterprise using MCP, its logistics Agent can directly access warehouse status via a GitHub MCP server, generating scheduling recommendations without rewriting code for each data source.

  III. The Current State of the MCP Ecosystem

  As of March 6, 2025, according to Anthropic's official website and community feedback:

  • Client Support: Over ten tools, including Claude Desktop, Cursor, and Continue, have integrated MCP.

  • Server Count: The community has contributed over 1,000 MCP servers, covering areas such as file systems, GitHub, and Google Drive.

  • Industry Adoption: Microsoft partners Block and Apollo have adopted MCP for internal systems.

  • Gartner Forecast: By 2026, 30% of enterprise AI projects will integrate external resources using standardized protocols like MCP, indicating significant market potential.

  IV. Technical Ethics and Data Security Challenges

  Despite its strengths, MCP faces several challenges:

  Data Security:

  • Risk: Improperly configured servers may leak sensitive data (e.g., corporate databases).

  • Current Status: MCP relies on host authorization but lacks a unified encryption standard.

  Technical Ethics:

  • Issue: AI Agents accessing user data via MCP may raise privacy concerns.

  • Solution: The community is developing "privacy filter" servers to limit the transmission of sensitive information.

  Performance Bottlenecks:

  • Current Status: JSON-RPC is inefficient when handling large files.

  • Improvement: Microsoft's Binary JSON experiment could provide insights for improving MCP.

  V. Get Started with MCP: A Quick Start Guide

  1. Installation: Download Cursor (an IDE that supports MCP).

  2. Configuration: Obtain the Filesystem MCP server from GitHub and run python server.py.

  3. Testing: Input "read local readme.md" to experience the Agent's contextual capabilities.

  VI. Community Suggestions

  Admittedly, MCP has many limitations and drawbacks, such as complex configuration, the need for manual server deployment and configuration files, high technical barriers, performance bottlenecks when handling large files or real-time tasks, insufficient documentation and stability for the current ecosystem of over 1,000 servers, security risks due to server isolation depending on implementation quality, lack of unified encryption and permission frameworks, limited functional coverage with basic tool and resource support that cannot meet professional scenarios, and last but not least, LLM dependency (strong binding to Claude for optimal support and understanding), which limits cross-platform universality and user experience based on LLM choice.

  Looking ahead, the community can gradually improve by lowering the barrier to entry, breaking LLM barriers, expanding the user base, and rapidly standardizing the process.