An Introduction to MCP Servers

The Model Context Protocol (MCP), introduced by Anthropic in November 2024, is rapidly transforming the way AI applications interact with external tools and data sources. As the AI landscape continues to evolve, MCP has emerged as a critical standard for enabling seamless integration between AI models and their environments.

In this blog post, we’ll explore what MCP is, why it matters, its architecture, and how developers can leverage it to build powerful AI-enabled applications.

What is MCP?

At its core, MCP is an open protocol that standardizes communication between AI systems (like Large Language Models or LLMs) and external tools, resources, and prompts. Think of MCP as the USB-C equivalent for AI systems — it provides universal rules for connecting clients (AI applications) with servers (external tools and data sources), ensuring interoperability across diverse ecosystems.

Key Features

• Standardized Communication: Uses JSON-RPC 2.0 for structured interaction between clients and servers.
• Interoperability: Allows any client to communicate with any server, regardless of who built either component.
• Actionable Context: Enables AI models to use tools, access resources, and follow predefined prompts.

Why Does MCP Matter?

MCP addresses critical challenges in AI integration:

• Context Augmentation: Enhances AI capabilities by providing structured access to external resources like databases or APIs.
• Tool Execution: Standardizes how AI models invoke external functions to perform tasks like API calls or file management.
• Scalability: Facilitates robust development of multi-agent systems by defining clear protocols for communication and orchestration.

Real-world examples include:

• Figma MCP servers simplifying design-to-code workflows for developers using IDEs like Cursor.
• Blender-MCP enabling prompt-assisted 3D modeling and scene manipulation.

MCP Architecture

MCP follows a client-server architecture with four key components:

Host: The host is the user-facing application (e.g., Claude desktop or IDE plugins) that connects to multiple MCP servers. It manages client-server lifecycles, user authorization, and context aggregation across clients.

Client: The client acts as an intermediary between the host and servers. Each client maintains:

• A dedicated connection with a single server.
• Capability management (tracking available tools/resources).
• Protocol negotiation during initialization.

Server: Servers provide specialized capabilities to enrich LLMs with external data and tools. They expose:

• Tools: Executable functions like API calls.
• Resources: Contextual data such as files or database records.
• Prompts: Templates guiding LLM interactions.

Base Protocol: The base protocol defines how hosts, clients, and servers communicate using JSON-RPC 2.0 messages:

• Requests initiate operations.
• Responses provide results.
• Notifications deliver one-way updates without requiring replies.

Building an MCP Server

Let’s look at a simple example of creating an MCP server in JavaScript:

Code Example:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
    name: "Weather Data Fetcher",
    version: "1.0.0"
});

server.tool('getWeatherDataByCityName', { city: z.string() },
    async ({ city }) => {
        const response = await fetch(`https://api.weather.com/${city}`);
        const data = await response.text();
        return {
          content: [{ type: "text", text: data }]
        };
    }
);

async function init() {
    const transport = new StdioServerTransport();
    await server.connect(transport);
    console.log("MCP Server is running...");
}

init();

Explanation

Server Initialization: The McpServer defines the server’s name and version.

Tool Definition: getWeatherDataByCityName fetches weather information from an external API based on the city name provided by the client.

Transport Layer: Uses StdioServerTransport for communication between the server and connected clients.

Advanced Features of MCP Servers

Transport Mechanisms

MCP supports multiple transport layers:

• Stdio: Ideal for local servers; uses standard input/output streams.
• HTTP with SSE (Server-Sent Events): Suitable for hosted servers; enables bidirectional communication over HTTP.

Lifecycle Management

The protocol includes structured phases:

• Initialization: Clients and servers negotiate capabilities.
• Operation: Normal communication occurs based on negotiated features.
• Shutdown: Graceful termination of connections.

Applications of MCP

MCP has broad applications across industries:

• AI Development: Enables LLMs to interact with APIs like Gmail, Slack, or Notion seamlessly.

• Multi-Agent Systems: Facilitates coordination among multiple agents working on complex tasks.

• User Interfaces: Powers IDE plugins that integrate directly with design tools like Figma or coding platforms like GitHub.

Conclusion

The Model Context Protocol is revolutionizing how developers build AI-enabled applications by standardizing context sharing and tool execution capabilities. Whether you’re developing intelligent assistants or multi-agent systems, MCP provides a robust foundation for scalable integration.

With its growing adoption across industries and open-source contributions, MCP is set to become a cornerstone of modern AI architectures. Start building your own MCP server today to unlock the full potential of this groundbreaking protocol!