What Is an MCP Server? The Developer's Guide to Model Context Protocol

You’re using Claude Code or Cursor. You need keyword data, a database query result, or some API response. So you open a browser, grab the data, paste it into your prompt, and wait.

That workflow breaks every time the data changes. MCP fixes it.

How MCP Servers Work

An MCP server is a program that runs on your machine (or remotely) and exposes “tools” that an AI assistant can call. The flow is simple:

1. You ask Claude a question that requires external data
2. Claude recognizes it needs a tool and calls the MCP server
3. The MCP server executes the request (API call, database query, file read)
4. Results flow back to Claude, which uses them to answer your question

The key insight: Claude decides when and how to use the tools. You don’t need to manually trigger anything.

You → Claude → MCP Server → External API/Data
                    ↓
              Results flow back
                    ↓
         Claude answers with real data

MCP vs. Traditional API Calls

Before MCP, connecting an AI to external tools meant writing custom integration code for each tool and each AI platform.

MCP standardizes the interface. Build one MCP server, and it works with Claude Code, Cursor, Windsurf, and any other MCP-compatible client.

Common MCP Server Use Cases

Code and Development

• Database queries: Ask Claude to check production data without writing SQL manually
• Git operations: Let Claude read commit history, diff branches, or check CI status
• Package management: Query npm, PyPI, or crate registries for version info

SEO and Marketing

• Keyword research: Pull keyword data from Google, Bing, and Reddit directly in your editor
• Content analysis: Check search intent, difficulty scores, and related keywords on the fly
• Competitor research: Query SEO APIs without leaving your terminal

Data and Analytics

• Dashboard queries: Pull metrics from analytics platforms
• Monitoring: Check server health, error rates, or deployment status
• Spreadsheet data: Read and analyze CSV or Google Sheets data

How to Set Up an MCP Server

Step 1: Choose Your Client

MCP works with several AI coding assistants:

• Claude Code (Anthropic’s CLI tool)
• Cursor (AI-powered code editor)
• Windsurf (Codeium’s AI editor)
• Claude Desktop (Anthropic’s desktop app)

Step 2: Install an MCP Server

Most MCP servers are distributed as npm packages. For example, to install Delulu9’s SEO MCP server:

npm install -g @delulu9/seo-mcp-server

Step 3: Configure Your Client

Add the server to your client’s MCP configuration. For Claude Code, add to .mcp.json in your project:

{
  "mcpServers": {
    "seo": {
      "command": "delulu9-seo-mcp",
      "args": ["--api-key", "YOUR_API_KEY"]
    }
  }
}

For Cursor, add the same configuration in Cursor’s MCP settings panel.

Step 4: Start Using It

Once configured, just ask naturally:

“What are the top keywords related to ‘project management software’?”

Your AI assistant will automatically call the MCP server, fetch the data, and present the results.

Building Your Own MCP Server

If you need a custom integration, building an MCP server is straightforward. The protocol handles:

• Tool registration: Declare what tools your server offers
• Schema definition: Define input/output types for each tool
• Request handling: Process tool calls and return results

Anthropic publishes SDKs for TypeScript and Python. A minimal MCP server can be under 50 lines of code.

from mcp import Server

server = Server("my-tool")

@server.tool("get_weather")
async def get_weather(city: str) -> str:
    # Your logic here
    return f"Weather in {city}: 72F, sunny"

server.run()

MCP Servers for SEO and Keyword Research

If you’re doing SEO work from your terminal or code editor, MCP servers eliminate the tab-switching workflow entirely.

Delulu9’s SEO MCP server gives you:

• Google, Bing, and Reddit keyword data in a single query
• Search intent classification (Informational, Commercial, Transactional)
• Difficulty scoring so you know which keywords to target
• $12/mo flat rate with 1,000 requests per hour

Instead of opening Ahrefs, running a search, exporting to CSV, and pasting into your editor, you just ask Claude.

Security Considerations

MCP servers run on your machine with your permissions. Keep these in mind:

• API keys: Store them in environment variables, not in config files committed to git
• Permissions: MCP servers can read files, make network requests, and execute commands. Only install servers you trust
• Network access: Some MCP servers call external APIs. Understand what data leaves your machine

The Future of MCP

MCP is still early. Anthropic open-sourced the protocol in late 2024, and the ecosystem is growing fast. Expect:

• More first-party MCP servers from tool vendors
• Better authentication and sandboxing
• Remote MCP servers that don’t require local installation
• Richer tool types beyond simple request/response

The direction is clear: AI assistants are becoming the interface layer, and MCP is how they connect to everything else.