How to Build and Host Your Own MCP Servers in Easy Steps?

Introduction

The Model Context Protocol (MCP) is revolutionizing how LLMs interact with external data sources and tools. Think of MCP as the “USB-C for AI applications” – a standardized interface that allows AI models to plug into various data sources and tools seamlessly. In this guide, I’ll walk you through building and hosting your own MCP servers, both privately and for public consumption, without requiring your code to be open-sourced.

Understanding MCP Architecture

• MCP Hosts: Applications like Claude Desktop or Cursor AI that want to access data through MCP
• MCP Clients: Protocol clients maintaining connections with servers
• MCP Servers: Lightweight programs exposing specific capabilities through the standardized protocol

Transport Methods:

• stdio for local communication
• Server-Sent Events (SSE) for cloud/remote deployment

Setting Up Your MCP Development Environment

Let’s start with setting up a basic MCP server in JavaScript/TypeScript, the most common implementation language:

import { createServer } from '@anthropic-ai/mcp-js';

const server = createServer({
  name: 'my-custom-mcp',
  version: '1.0.0',
  capabilities: [{
    name: 'my-custom-capability',
    description: 'Provides access to custom data',
    methods: {
      search: async (query) => {
        return { results: ['result1', 'result2'] };
      }
    }
  }]
});

server.listen();

For Python enthusiasts, here’s how to set up a simple MCP server. Refer to this documentation for installation and usage:

from mcp.server.fastmcp import FastMCP

# Create an MCP server
server = FastMCP("my-python-mcp")

# Add capabilities like tools
@server.tool()
def example_tool(arg1: str) -> str:
    """Example tool description"""
    return f"Processed: {arg1}"

# Add dynamic resources
@server.resource("example://{param}")
def get_example(param: str) -> str:
    """Example resource"""
    return f"Resource data for {param}"

# Run the server
if __name__ == "__main__":
    server.run()

Deployment Options

1. Local Installation (Private Use)

For personal use without publishing:

# For JavaScript/TypeScript projects
npm install -g .

# For Python projects using pip
pip install -e .

# For Python projects using uv (recommended)
uv pip install -e .

The -e flag (editable mode) installs the package locally in development mode, so your changes to the code are immediately reflected without needing to reinstall. This works for both pip and uv.

To add MCP as a dependency to your existing Python project:

# Using uv (recommended)
uv add "mcp[cli]"

# Using pip
pip install mcp

For running MCP development tools:

# Using uv
uv run mcp

# Using pip-installed version
mcp

This approach maintains the advantages of using uv (faster, more reliable dependency management) while still providing the equivalent functionality to the pip install -e . command for local development without publishing your MCP server.

2. Self-Hosted Cloud Deployment (SSE Transport)

For remote access via SSE, modify your server:

from mcp.server.fastmcp import FastMCP
import uvicorn

server = FastMCP("my-python-mcp")

if __name__ == "__main__":
    uvicorn.run(server.sse_app(), host="0.0.0.0", port=3000)

3. Deploying on Virtual Machines

To deploy on a VM:

• Provision a VM on AWS, GCP, or Azure
• Install Node.js/Python and dependencies
• Deploy your MCP server with SSE transport
• Configure firewall rules

Example Nginx configuration:

server {
    listen 80;
    server_name your-domain.com;

    location / {
        proxy_pass http://localhost:3000;
    }
}

Security Considerations

• Authentication: Use API keys or OAuth
• Rate Limiting: Prevent abuse
• HTTPS: Always use SSL/TLS
• Logging & Monitoring: Track usage and detect anomalies

Conclusion

MCP allows AI applications to interact with external data sources and tools seamlessly. Whether you choose local installation, cloud deployment, or managed services, MCP provides flexible solutions for your AI integrations.