Model Context Protocol (MCP): A New Standard for AI Application and External Data Integration

The Model Context Protocol (MCP) introduced by Claude in late 2024 has quickly gained widespread attention and recognition from developers and the community. As a developer who has been involved in several small AI projects over the past year, I deeply understand the challenges of integrating AI models into existing systems or third-party services.

The emergence of MCP comes at the perfect time, not only solving these pain points but also bringing a vision of an open, standardized ecosystem. Below, I’ll analyze why it has been widely accepted from a developer’s perspective, combining MCP’s working principles and advantages.

I. Pain Points in AI Integration Development: Why Do We Need MCP?

Integrating models with external systems is often a major challenge in AI project development. From my personal experience, whether connecting to local databases or calling third-party APIs, the entire process is full of complexity and uncertainty. While there are some tools and frameworks in the market trying to solve this problem, the results are not satisfactory:

• LangChain and LlamaIndex
  These two open-source projects promote the concept of “implementing AI features in a few lines of code,” which is convenient for demos. However, once business requirements become complex, their shortcomings become apparent:

  • Over-abstracted Code: While high-level encapsulation lowers the entry barrier, in actual development, this design makes code difficult to debug and extend, resulting in poor programming experience.
  • Chaotic Ecosystem: The projects focus too much on commercialization, neglecting long-term community ecosystem building, with documentation and updates often failing to keep up with actual needs.

• Vercel AI SDK
  In comparison, Vercel AI SDK has more elegant code abstraction, particularly in frontend UI integration and some AI feature encapsulation. However, its limitations are also obvious:

  • Next.js Coupling: Deep coupling with the Next.js framework, insufficient support for other frameworks or languages, limiting its applicability.

These tools, while each having their highlights, fail to provide a unified, flexible solution. Developers still need to reinvent the wheel in different projects, wasting significant time and effort.

II. The Launch of MCP: Perfect Timing, Outstanding Advantages

Claude’s timing in launching MCP couldn’t have been better, as it captures developers’ urgent need for standardization and openness. Here are several key reasons why MCP has been well-received:

1. Claude’s Brand Influence

As an outstanding language model, Claude Sonnet 3.5 has established a good reputation among developers. This trust has laid the foundation for MCP’s promotion. Developers are willing to try a protocol backed by Anthropic (Claude’s development company) because they believe the Claude team can continuously deliver high-quality technical solutions.

2. The Appeal of Open Standards

MCP is an open protocol, not bound to any specific model or framework. This openness has attracted participation from many companies and communities. Unlike traditional AI integration solutions that are often limited to specific platforms, MCP provides a universal bridge benefiting both service providers and developers:

• For Service Providers: Can open their APIs and features based on MCP, integrating into a larger ecosystem.
• For Developers: Can enhance AI Agent capabilities directly using existing MCP services without building integration logic from scratch.

3. Solving the Wheel Reinvention Problem

MCP’s standardized design allows developers to reuse community-contributed resources. For example, once an MCP server supporting GitHub operations is developed, other developers can call it directly without reimplementing. This “develop once, use many times” pattern greatly improves efficiency.

III. How Does MCP Work? — From Architecture to Examples

To understand MCP’s popularity, we need to look at how it works. MCP’s design isn’t complicated, but it’s very practical. It connects AI models to the external world through a clear architecture.

MCP Core Architecture

MCP consists of five main components:

• MCP Hosts: Applications running AI models, such as Claude Desktop or Cursor.
• MCP Clients: Modules responsible for communicating with servers within Hosts.
• MCP Servers: The core part, providing tools, data, and context to Clients through standardized protocols.
• Local Data Sources: Directly accessible data like local files and databases.
• Remote Services: External APIs or services, such as GitHub or Slack.

Among these, the MCP Server is the soul of the protocol. It acts as a “mediator,” telling the AI Agent what services and data are available, and the AI Agent executes specific tasks through function calling.

Evolution from AI Chat to AI Agent

MCP’s value is also reflected in how it drives AI automation:

• AI Chat: Only provides suggestions, requiring manual operations (like copy-paste) from users.
• AI Composer: Can modify code automatically but still needs human confirmation and has limited functionality.
• AI Agent: Fully automated, capable of completing complex tasks independently, such as reading Figma designs, generating code, debugging, and submitting to GitHub.

The role of MCP Server is to support AI Agents, letting them know “what can be done” and “how to do it.”

A Simple Example: GitHub MCP Server

Suppose we want an AI Agent to automatically complete these tasks: search GitHub repositories, check Issues, determine if it’s a known bug, and decide whether to submit a new Issue. We can create a GitHub MCP Server with these capabilities:

• search_repositories: Search repositories.
• search_issues: Search Issues.
• create_issue: Create new Issues.

Here’s a simplified implementation:

const server = new Server({ name: "github-mcp-server" });

server.setRequestHandler("listTools", async () => ({
  tools: [
    { name: "search_repositories", description: "Search GitHub repositories" },
    { name: "search_issues", description: "Search issues in repositories" },
    { name: "create_issue", description: "Create a new issue" },
  ],
}));

server.setRequestHandler("callTool", async (request) => {
  switch (request.params.name) {
    case "search_repositories":
      return await searchRepositories(request.params.arguments.query);
    case "search_issues":
      return await searchIssues(request.params.arguments);
    case "create_issue":
      return await createIssue(request.params.arguments);
  }
});

async function searchRepositories(query) {
  const url = `https://api.github.com/search/repositories?q=${query}`;
  const response = await fetch(url);
  return await response.json();
}

This server helps AI Agents understand how to interact with GitHub through tool descriptions (description) and parameter requirements (inputSchema). Eventually, Agents can autonomously call these features based on task requirements.

IV. MCP’s Ecosystem Value: More Powerful AI Agents

MCP’s true potential lies in its ability to support more complex AI Agents. Consider a real scenario: a user inputs “check local error logs and send relevant Issues to Slack.” The AI Agent needs to:

1. Call Local Log Server to read logs.
2. Call GitHub Server to search related Issues.
3. Call Slack Server to send messages.

MCP’s standardized protocol enables seamless cooperation between these servers, allowing AI Agents to dynamically decide next steps based on returned results. This modular design not only improves flexibility but also reduces development costs.

V. Conclusion: Why Has MCP Won Favor?

Claude’s MCP has been widely accepted because it captures developers’ core needs:

• Solves Pain Points: Simplifies the integration process between AI and external systems.
• Open Ecosystem: Attracts community and enterprise participation through standardization.
• Strong Practicality: Provides clear implementation paths from simple tools to complex Agents.

For developers, MCP is not just a technical protocol but a tool that liberates productivity. In the future, as more MCP servers are developed and the ecosystem matures, it has the potential to become an industry standard in AI integration. If you’re interested in AI development, give MCP a try - it might become the secret weapon for your next project!