What Is the Model Context Protocol (MCP) and How Can It Supercharge Your AI Projects?

Since mid‑February the MCP protocol has gained rapid popularity, with its GitHub star count accelerating and even OpenAI founder Sam Altman announcing a major update to the Agents SDK that supports Anthropic's MCP service.

What is MCP? MCP stands for Model Context Protocol , an open standard released by Anthropic (the company behind Claude) in November 2024. It enables large language models to seamlessly connect to third‑party data sources such as content repositories, business tools, and development environments, providing richer context for more accurate and relevant responses.

MCP acts like a universal adapter or “USB‑C” for AI models: developers create a single MCP server (an interface adapter) that can be used by any MCP‑compatible model, eliminating the need to write separate connectors for each model.

The protocol also includes SSE (Server‑Sent Events) for real‑time, bidirectional communication, allowing models not only to query data but also to trigger actions.

Key Differences from Traditional APIs

Single unified protocol – one integration connects multiple services.

Dynamic discovery – AI models can automatically detect and use available tools without hard‑coding each interface.

Bidirectional communication – similar to WebSockets, enabling both queries and proactive operations.

In practice, MCP turns external tools into the “hands and feet” of an AI agent, while the LLM serves as the brain.

2. Blender MCP

Using a single prompt, Claude can automatically launch Blender, convert a 2D image into a 3D model, and even generate an interactive web scene—all with one instruction.

3. Manus

Manus employs a similar tool‑calling approach (though not directly using MCP) to execute actions like browsing the web, recording Amazon store interactions, or saving job listings to a file.

4. MCP Service Marketplace

Numerous community‑driven services are being cataloged, offering ready‑to‑install MCP adapters for various data sources and tools. Example repositories include modelcontextprotocol/servers .

Practical Hands‑On Guide

1. Convert Design Mockups to Front‑End Code

Using the Figma‑Context‑MCP framework, you can paste a Figma file URL to a large model and receive a previewable HTML page.

Install MCP (the framework) locally.

Register the Figma file via a JSON configuration and obtain an API key.

After providing the Figma URL, the model generates the front‑end code, which can be previewed directly.

Note that the generated code may still rely on Claude for layout decisions, so occasional inconsistencies are expected.

2. Generate AI‑Powered News Cards

By performing a single web‑search query, the model retrieves the latest news data, applies a Figma‑style template, and produces a styled news card.

MCP Client Tools

Many models cannot directly use MCP without a plugin layer (similar to function calling). Open‑source models like Qwen‑max support MCP. Cherry Studio is an open‑source client that supports SSE and npx installation.

After installing Cherry Studio, configure the MCP server, install dependencies (UV/Bun), and add the server via the UI.

Example JSON configuration for an npx‑based MCP server:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sequential-thinking"]
    }
  }
}

Enable the server, select a model that supports MCP, and you’ll see successful calls in the UI.

Overall, MCP represents a significant milestone in AI development, shifting models from passive responders to active collaborators that can integrate and act upon external tools and data sources.