The Full Developer's Guide to Model Context Protocol

Yusuf Ishola· March 13, 2025

Anthropic's Model Context Protocol (MCP) is gaining massive traction as a game-changing standard for connecting Large Language Models (LLMs) to external data sources and tools.

Let's take a deep dive into just exactly what MCP is, and how you can begin using it in your projects.

Table of Contents

• What is Model Context Protocol (MCP)?
• Core Capabilities of MCP
• Getting Started with MCP
• Common MCPs and Their Use Cases
• Where to Find MCPs
• How to Debug and Troubleshooting MCPs
• Conclusion

What is Model Context Protocol (MCP)?

MCP is an open protocol that standardizes how applications provide context to LLMs.

Think of MCP like a "USB-C port for AI applications". Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to external data sources and tools.

MCP Architecture

The protocol follows a client-server architecture with three main components:

1. Hosts: LLM applications (like Claude Desktop or IDEs) that initiate connections
2. Clients: Components within hosts that maintain connections with servers
3. Servers: Lightweight programs that expose specific capabilities through the protocol

For example, in a workflow using Claude to analyze company sales data stored in a PostgreSQL database:

• The host is the Claude Desktop app
• The client is the MCP client module within Claude Desktop that manages connections to MCP servers
• The server is the PostgreSQL MCP server that securely connects to the company database

For details, check out the official documentation.

Why MCP Exists

Before MCP, developers had to build custom integrations from scratch whenever they wanted LLMs to access external systems (files, APIs, databases).

Each implementation was different, required significant code, and wouldn't work with desktop apps. MCP provides a standardized framework for these integrations that works consistently across implementations, with a common communication protocol between systems and AI tools.

Core Capabilities of MCP

MCP offers three primary capabilities:

1. Resources: File-like data that LLMs can read (API responses, file contents)
2. Tools: Functions that LLMs can call to perform actions
3. Prompts: Pre-written templates that help users accomplish specific tasks

Getting Started with MCP

We will walk you through 3 implementation approaches to get started with MCP based on your needs and experience level.

• Basic 🟢: Using pre-built MCP servers with Claude Desktop
• Intermediate 🟡: Building custom MCP servers with Cloudflare Workers
• Advanced 🔴: Creating custom MCP clients and servers from scratch

Basic Setup: Connecting Pre-built MCP Servers (🟢)

Let's set up a simple MCP configuration to access file system and web search capabilities with Claude Desktop.

Step 1: Install Claude Desktop

Download and install Claude Desktop.

Step 2: Configure MCP Servers

Create or edit the configuration file at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the following configuration:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/files"]
    },
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "your_brave_api_key_here"
      }
    }
  }
}

Step 3: Restart Claude Desktop

After saving the configuration, restart Claude Desktop for the changes to take effect.

Step 4: Test Your Setup

Now you can ask Claude to use these tools:

• "Search the web for the latest news on Anthropic and save a summary to my documents folder."
• "List all files in my allowed files directory."

When Claude needs to use an MCP tool, it will request your permission before proceeding.

Intermediate Setup: Building Custom MCP Servers with Cloudflare Workers (🟡)

Cloudflare Workers offer a simplified approach to building MCP servers. Let's create an image generation server:

Step 1: Set Up Cloudflare Worker and Configure to Support MCP

# Create a new Cloudflare project
npx create-cloudflare@latest mcp-imagegen
cd mcp-imagegen

# Install the workers-mcp package
npm install workers-mcp

# Configure to support MCP
npx workers-mcp setup

Step 2: Implement the Image Generation Server

Replace the content of src/index.ts with:

import { WorkerEntrypoint } from 'cloudflare:workers'
import { ProxyToSelf } from 'workers-mcp'

export default class ImageGenerator extends WorkerEntrypoint<Env> {
  /**
   * Generate an image using an AI model.
   * @param prompt {string} A text description of the image you want to generate.
   * @param steps {number} The number of diffusion steps; higher values can improve quality but take longer.
   * @return {string} URL to the generated image.
   */
  async generateImage(prompt: string, steps: number = 30): Promise<Response> {
    const response = await this.env.AI.run('@cf/black-forest-labs/flux-1-schnell', {
      prompt,
      steps,
    });
    
    // Convert from base64 string
    const binaryString = atob(response.image);
    // Create byte representation
    const img = Uint8Array.from(binaryString, (m) => m.codePointAt(0)!);
    
    return new Response(img, {
      headers: {
        'Content-Type': 'image/jpeg',
      },
    });
  }

  /**
   * @ignore
   */
  async fetch(request: Request): Promise<Response> {
    return new ProxyToSelf(this).fetch(request)
  }
}

Step 3: Deploy Your Worker

npm run deploy

Step 4: Configure Claude Desktop (Optional)

To add your newly-created MCP to Claude Desktop for example, update your claude_desktop_config.json to include your Cloudflare Worker:

{
  "mcpServers": {
    "image-generator": {
      "command": "npx",
      "args": ["wrangler", "dev", "--local"]
    }
  }
}

Now you can ask Claude to generate images, and it will use your Cloudflare Worker to do so!

For more information on configuring MCPs with other Clients, such as Cursor, check out the documentation.

Advanced Setup: Creating Custom MCP Clients (🔴)

Building an MCP client allows you to create a custom application to connect to MCP servers. For detailed instructions, visit Anthropic's official guide to learn how to build a client in Python, NodeJS, or Java.

Common MCPs and Their Use Cases

Where to Find MCPs

Here are several sources for finding pre-built MCP servers for your LLM applications:

• Example Servers on the MCP website (a curated list)
• Central hub of MCP servers by Mintlify
• NPM packages with the pattern @modelcontextprotocol/server-*
• Python implementations with naming convention mcp-server-* on PyPI
• Glama AI's collection of MCP servers for various integrations
• Smithery.ai directory of MCP servers for AI development
• Cursor Directory for Cursor IDE-specific MCPs
• LM Systems Marketplace collection of MCP servers and tools

How to Debug and Troubleshooting MCPs

1. Use the MCP Inspector - An interactive tool for directly testing MCP servers, their resources, tools, and prompts outside of any client application

2. Use Claude Desktop's Developer Tools - Access Chrome DevTools within Claude Desktop to inspect client-side behavior, network requests, and console logs

3. Analyse Logs - View detailed logs generated by MCP servers and Claude Desktop to identify connection issues and runtime errors

4. Use the MCP CLI - Use command-line tools to inspect and test MCP servers and their capabilities

5. Implement Server-Side Logging - Implement custom logging in your MCP server to track execution flow, input validation, and error states

6. Use Network Analysis Tools - For HTTP-based MCP servers, use proxies and network analyzers to inspect the communication between clients and servers

7. Perform Standalone Testing - Test MCP servers in isolation before integrating them with clients to identify server-specific issues

8. Environment Validation - Verify that environment variables, file paths, and permissions are correctly configured

For details, check out the official documentation.

Conclusion

MCP represents a significant step forward in connecting AI models to external systems.

By providing a standardized way for LLMs to interact with data sources and tools, MCP makes it easier to build powerful, context-aware AI applications—including, of course, powerful agentic systems.

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