Guide For: Developers who want to enhance their AI development tools with MCP but haven't set it up yet

How to Use MCP Servers in Your Development Workflow

A practical introduction to the Model Context Protocol — what it is, what problems it solves, and how to start using MCP servers today.

What This Solves

The Model Context Protocol is well-specified but the practical 'how do I actually use this in my workflow' guidance is scattered.

What MCP Actually Is

The Model Context Protocol is a standard for connecting LLM clients to external data and tools. Think of it as a USB standard for AI: instead of every LLM client implementing custom integrations for every data source, MCP provides a common interface.

An MCP server exposes tools (functions the LLM can call), resources (data the LLM can read), and prompts (reusable interaction templates). The client — Claude Desktop, Continue.dev, your custom app — handles the MCP connection and makes the server's capabilities available to the LLM.

The Problem MCP Solves

Before MCP, if you wanted Claude to be able to search your codebase and query your database, you had to implement both integrations separately for each LLM client you used. Claude Desktop needed one implementation. Your VS Code extension needed another.

MCP standardizes the interface so that a GitHub MCP server you install works in Claude Desktop today and in any future MCP-compatible client tomorrow. Write the integration once.

Setting Up Your First MCP Server

The GitHub MCP server is the best starting point for most developers. It exposes GitHub's API — code search, issues, PRs — as tools your LLM can call.

Installation

# Clone the official GitHub MCP server
git clone https://github.com/github/github-mcp-server
cd github-mcp-server
npm install
npm run build

Claude Desktop Configuration

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "github": {
      "command": "node",
      "args": ["/path/to/github-mcp-server/dist/index.js"],
      "env": {
        "GITHUB_TOKEN": "your-github-pat"
      }
    }
  }
}
Restart Claude Desktop. You'll see a tool indicator in the conversation, and Claude can now search your repositories, read issues, and review PRs.

Which MCP Servers to Install First

For AI-assisted development, prioritize:

  • GitHub MCP Server — code search, PR review, issue management
  • File system MCP server — lets Claude read and write local files
  • PostgreSQL or SQLite MCP server — direct database access for data work

      • When MCP Is the Right Abstraction

      MCP is the right choice when you want the same integration available across multiple LLM clients. If you're building a one-off integration for a specific application, direct API calls are simpler.

      Common Mistakes

    1. Running MCP servers with broad file system access on shared machines — scope your file system server to project directories, not your entire home directory
    2. Expecting MCP to make your LLM smarter — MCP expands what the LLM can access, not how well it reasons about what it finds
    3. Installing too many servers at once — start with one, learn the patterns, then add more