MCP Servers Explained: What They Are and Why They Matter

The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.

What Is MCP?

Model Context Protocol (MCP) is a standard way for AI assistants to connect to external tools and data sources. Think of it like USB for AI — a universal plug that lets any AI assistant talk to any service.

Before MCP, every AI tool had its own way of connecting to things. Claude had its tool-use API. ChatGPT had plugins. Cursor had its own integration system. None of them talked to each other.

MCP changes that. One protocol, many AI clients, infinite tools.

Why Should You Care?

If you use AI coding tools: MCP lets your assistant access databases, APIs, file systems, browsers, and more — directly. No copy-pasting, no switching windows.

If you build AI tools: MCP means you build one integration and it works with Claude Code, Cursor, Windsurf, and any other MCP-compatible client. Build once, deploy everywhere.

If you run a business: MCP servers let you connect AI assistants to your internal tools — Slack, Jira, databases, custom APIs — safely and with access controls.

How MCP Works (Simple Version)

Your AI Assistant  <-->  MCP Client  <-->  MCP Server  <-->  External Service
   (Claude Code)      (built into        (you build       (Slack, DB,
                        the client)        or install)       GitHub, etc.)

1. MCP Server — A small program that exposes tools and data. It says "I can do X, Y, and Z" and describes what inputs it needs.
2. MCP Client — Built into your AI assistant. It discovers available servers and their capabilities.
3. Your AI Assistant — Sees the available tools and decides when to use them based on your instructions.

When you ask Claude Code "check my latest GitHub PRs," it:

1. Sees the GitHub MCP server is available
2. Calls the list_pull_requests tool on that server
3. Gets the results back
4. Formats them into a human-readable response

You never see the plumbing — it just works.

MCP Servers You Can Use Today

These are production-ready and widely used:

File & Development

• Filesystem — Read, write, search files outside the sandbox
• Git — Repository operations, diff, log, branch management
• GitHub — PRs, issues, repos, actions

Productivity

• Slack — Read channels, send messages, search history
• Google Drive — Read and search documents
• Linear — Issue tracking and project management

Data

• PostgreSQL — Query databases directly
• SQLite — Local database operations
• Pinecone — Vector database for semantic search

Browser & Web

• Playwright — Full browser automation, screenshots, form filling
• Fetch — HTTP requests to any API

Media

• Remotion — Generate videos, images, speech, music programmatically

Setting Up Your First MCP Server

Most AI coding tools let you configure MCP servers in a JSON file.

For Claude Code

Create or edit .mcp.json in your project root:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/dir"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "your-token-here"
      }
    }
  }
}

Restart Claude Code, and the servers are available immediately. You can verify by asking Claude Code "what MCP tools do you have?"

For Cursor

Similar JSON config in .cursor/mcp.json:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "your-token-here"
      }
    }
  }
}

Building Your Own MCP Server

If you have an internal tool or API that you want your AI to access, you can build a custom MCP server in about 30 minutes.

TypeScript (Most Common)

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({
  name: "my-custom-server",
  version: "1.0.0",
}, {
  capabilities: { tools: {} }
});

// Define a tool
server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "get_weather",
    description: "Get current weather for a city",
    inputSchema: {
      type: "object",
      properties: {
        city: { type: "string", description: "City name" }
      },
      required: ["city"]
    }
  }]
}));

// Handle tool calls
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "get_weather") {
    const city = request.params.arguments.city;
    // Your logic here
    return {
      content: [{ type: "text", text: `Weather in ${city}: 25C, sunny` }]
    };
  }
});

// Start the server
const transport = new StdioServerTransport();
await server.connect(transport);

Python

from mcp.server import Server
from mcp.server.stdio import stdio_server

app = Server("my-custom-server")

@app.list_tools()
async def list_tools():
    return [{
        "name": "get_weather",
        "description": "Get current weather for a city",
        "inputSchema": {
            "type": "object",
            "properties": {
                "city": {"type": "string"}
            },
            "required": ["city"]
        }
    }]

@app.call_tool()
async def call_tool(name, arguments):
    if name == "get_weather":
        return f"Weather in {arguments['city']}: 25C, sunny"

async def main():
    async with stdio_server() as (read, write):
        await app.run(read, write)

MCP Security: What You Need to Know

MCP servers run on your machine with your permissions. This means:

1. Access control matters. Only give MCP servers access to directories and services they need. The filesystem server takes an allowed directory as an argument — use it.

2. Environment variables for secrets. Never hardcode API keys in MCP config. Use environment variables.

3. Review before installing. Third-party MCP servers are just npm/pip packages. Review the source before running them, especially if they access sensitive data.

4. User approval. Good MCP clients (like Claude Code) prompt you before executing potentially destructive tool calls. Don't disable these safeguards unless you know what you're doing.

Where MCP Is Heading

MCP is still early but moving fast. Key trends:

• More clients adopting it — Claude Code, Cursor, Windsurf, and others all support MCP now
• Enterprise MCP servers — Companies building internal MCP servers for their teams
• MCP marketplaces — Directories of pre-built servers you can install with one command
• Authentication standards — OAuth and API key management built into the protocol

The protocol is open source and governed by Anthropic with community input. It's the closest thing the AI industry has to a universal standard for tool integration.

If you're building anything in the AI space, understanding MCP isn't optional — it's becoming the default way AI tools connect to the world.