The Model Context Protocol: AI’s USB-C for Tools and Workflows

Definition: Model Context Protocol (MCP) is a standard by Anthropic that simplifies how Large Language Models (LLMs) connect to external tools and services like APIs, databases, or apps (e.g., Slack, Google Drive).

Purpose: Acts as a “universal translator” to make LLMs more powerful by enabling seamless communication between various tools.

Analogy: Think of MCP as a USB-C port for AI—one connection method that works with many systems, reducing custom integration hassles.

LLM Limitations

• LLMs (e.g., ChatGPT, Claude) can only generate text or answer questions based on training data.
• They can’t perform actions like sending emails or fetching real-time data without external tools.

Current Challenges

• Connecting LLMs to tools is complex. Each tool has a unique “language” (API structure).
• Combining multiple tools for cohesive AI workflows is time-consuming and error prone.

MCP’s Solution

• Creates a unified layer that translates tool-specific “languages” into a standard format LLMs understand.
• Simplifies integration, making LLMs more capable of real-world tasks.

Ecosystem Components

• User: Initiates a request (e.g., “Can you find my latest Google Drive files?”).
• AI Agent: Receives user input and forwards it to an MCP Client. Examples: GPT (OpenAI), Claude (Anthropic).
• MCP Client️: Translates the AI Agent’s request into a structured format and communicates with the MCP Server using the MCP Protocol.
• MCP Protocol: A standardized communication layer between Clients and Servers.
• MCP Server: Operated by a service provider; interprets the request, interacts with external services, and formats the response.
• Service/API: External tools or data sources (e.g., Google Drive, Slack, Google Maps, GitHub).

Process

• User Request: The user asks an AI assistant (e.g., GPT or Claude) to perform a task involving external data or tools.
• AI Agent Delegation: The AI Agent forwards the request to an MCP Client integrated with it.
• Structured Client Request: The MCP Client creates a standardized request and sends it to the designated MCP Server.
• Server Handling:
  • The MCP Server formats and sends an HTTP request to the correct API.
  • It also manages authentication, error handling, and validation of responses.
• External API Call: The third-party service (e.g., Slack, Google Drive) processes the request and returns data or an error.
• Response Delivery: The response flows back through the MCP Server → Client → AI Agent → User.

Simplified Integration: Saves developers time by reducing the need for custom connections.

Scalability: Easily add new tools without rebuilding integrations.

Futureproofing: MCP servers handle API updates, minimizing disruptions.

Enhanced Capabilities: Moving LLMs closer to “Jarvis-like” assistants for complex, multi-step tasks.

Sequential Thinking MCP Server

What It Is:

• A tool that breaks complex tasks into clear, logical steps.
• Analyzes project structures or plans features systematically.

How It’s Used:

1. Feature Planning: When you ask ”How do I implement user authentication?”, it thinks through each step:
   • Analyze the current project structure
   • Identify where the auth logic should live
   • Plan the API endpoints needed
   • Design data models required
   • Map out the UI flow and components
2. Architecture Decisions: When asked ”Should I use MVVM or MVC?”, it evaluates:
   • Current project complexity
   • Team expertise and preferences
   • Testing requirements
   • Scalability needs
   • Provides a structured recommendation with reasoning

Benefits:

• Simplifies Tasks: Breaks down big features into manageable, clear steps.
• Improves Clarity: Helps avoid missing essential implementation details.
• Speeds Up Coding: Provides structured guidance for development.

Memory MCP Server

What It Is:

• Stores project details (e.g., structure, component logic) in a knowledge graph or JSON.
• Recalls information across sessions for consistent assistance.

How It’s Used:

• Stores context like:
• Retrieves context when asked, e.g., “Does the bottom sheet component support swipe gestures?” or “Can you implement a POST request when I click this button?”
• Creates a memory.json file (or any custom filename) when provided with a file path.
• It is heavily driven by declarative “rules” that guide the agent’s behavior. These rules help the agent understand how to interpret stored information, decide what’s relevant, and respond accurately. This ensures consistency, domain alignment, and intelligent memory usage during task execution.

Benefits:

• Context Continuity: The AI remembers your project details and code structure from the memory file, based on how the context was previously stored.
• Time Savings: Avoids repetitive explanations or context resets.
• Smarter Suggestions: Offers code completions and advice tailored to your actual project.

Getting started with MCP servers is straightforward, whether you’re a developer looking to enhance your AI workflow or someone wanting to explore what’s available.

Step 1: Discover Available MCP Servers

Explore MCP Marketplaces:

• MCP.so – The largest collection of MCP servers with over 15,000 indexed servers. Browse by categories like Featured, Latest, Clients, Hosted, and Official servers. Perfect for discovering popular servers like Context7, Sequential Thinking, and Filesystem servers.
• MCPmarket.com – Another comprehensive marketplace for finding and comparing different MCP servers and their capabilities.

What You’ll Find:

• Featured Servers: Top-rated and most popular MCP servers
• Categories: Organized by function (file management, web search, databases, etc.)
• Client Compatibility: See which AI tools (Cursor, Claude, VS Code) work with each server

Step 2: Choose Your AI Client

Popular MCP-Compatible Clients:

• Cursor – AI-powered code editor with built-in MCP support
• Claude Desktop – Anthropic’s AI assistant with native MCP integration
• VS Code – With MCP extensions for enhanced coding capabilities
• Windsurf – Purpose-built IDE for AI-enhanced development

Step 3: Install and Configure

Basic Installation Process:

1. Choose an MCP Server from the marketplaces above
2. Configure in Your AI Client by adding the server to your settings
3. Start Using – Once the server tools are available in your AI assistant, you can direct the AI Agent to access them. For example, you might prompt it to use the Sequential Thinking MCP server to break down a task step by step, or the Memory MCP server to store a specific component in the memory file for future reference.

Step 4: Start Simple

Recommended First Servers:

• Filesystem MCP: For basic file operations and code management
• Context7 MCP: For accessing up-to-date documentation
• Sequential Thinking MCP: For breaking down complex coding tasks

Pro Tips:

• Start with 1-2 servers to avoid overwhelming your workflow
• Check server compatibility with your preferred AI client
• Make sure the AI agent knows it can use the provided MCP server—for instance, by prompting “use sequential thinking to achieve this task”.

MCP bridges the gap between large language models (LLMs) and real-world tools like Slack, Google Drive, and GitHub, enabling AI to move beyond static text and perform dynamic actions. It functions like a USB-C for AI-in-one protocol that supports many tools without the need for custom glue code. By leveraging ready-to-use MCP servers that handle API communication, authentication, and error management, developers save significant time and effort. This streamlined approach makes AI workflows more scalable and future-proof, accelerating the development of more autonomous and context-aware assistants.

• Model Context Protocol (MCP), clearly explained (why it matters) By Greg Isenberg