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 und 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:

  • Überabstrahierter Code: Hohe Abstraktion senkt zwar die Einstiegshürde, erschwert in der Praxis aber Debugging und Erweiterbarkeit und führt zu einer schlechten Programmerfahrung.
  • Chaotisches Ökosystem: Zu starker Fokus auf Kommerzialisierung; die langfristige Community‑Ökosystem‑Pflege wird vernachlässigt, Dokus und Updates hinken realen Anforderungen hinterher.

• 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‑Kopplung: Enge Bindung an das Next.js‑Framework; unzureichende Unterstützung für andere Frameworks/Sprachen, was die Anwendbarkeit einschränkt.

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:

• Für Dienstanbieter: Können ihre APIs und Funktionen MCP‑konform öffnen und in ein größeres Ökosystem integrieren.
• Für Entwickler: Können vorhandene MCP‑Dienste direkt nutzen, um Agent‑Fähigkeiten zu erweitern, ohne Integrationslogik neu zu bauen.

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: Anwendungen, die KI‑Modelle ausführen, z. B. Claude Desktop oder Cursor.
• MCP‑Clients: Module innerhalb der Hosts, die mit Servern kommunizieren.
• MCP‑Server: Kernkomponente, die Clients über standardisierte Protokolle Tools, Daten und Kontext bereitstellt.
• Lokale Datenquellen: Direkt zugängliche Daten wie lokale Dateien und Datenbanken.
• Remote‑Dienste: Externe APIs/Services, z. B. GitHub oder 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: Gibt nur Vorschläge, Nutzer müssen manuell handeln (z. B. Copy‑Paste).
• AI Composer: Kann Code automatisch ändern, benötigt aber Bestätigung; Funktionsumfang begrenzt.
• AI Agent: Vollautomatisiert; kann komplexe Aufgaben eigenständig erledigen (Figma lesen, Code generieren, debuggen, zu GitHub einreichen).

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: Repositories durchsuchen.
• search_issues: Issues durchsuchen.
• create_issue: Neues Issue erstellen.

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. Local Log Server aufrufen, um Logs zu lesen.
2. GitHub Server aufrufen, um relevante Issues zu finden.
3. Slack Server aufrufen, um Nachrichten zu senden.

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:

• Schmerzpunkte lösen: Vereinfacht die Integration zwischen KI und externen Systemen.
• Offenes Ökosystem: Standardisierung zieht Community‑ und Enterprise‑Beteiligung an.
• Hohe Praxistauglichkeit: Klare Umsetzungswege von einfachen Tools bis zu komplexen 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!