MCP Help 001

Project Overview

MCP Help 001 is a demonstration Model Context Protocol (MCP) server that showcases how to create custom MCP servers with integrated web interfaces, browser automation tools, and analytics tracking. This project serves as both a functional tool and a comprehensive learning resource for developers interested in MCP development.

What is Model Context Protocol (MCP)?

The Model Context Protocol is a standardized way for AI assistants to interact with external tools and data sources. It enables AI systems like Cursor IDE to seamlessly integrate with custom servers that provide specific functionality. As of 2025, MCP has become integral to AI systems, facilitating interactions between large language models (LLMs) and external tools.

Why Use MCP?

The Creation Process

This section documents the actual conversation and process that led to the creation of MCP Help 001, providing insight into how AI-assisted development works in practice.

Initial Request

AI Research and Analysis

The AI assistant began by researching existing MCP implementations in the codebase to understand the proper structure and requirements:

Research Process:
1. Analyzed existing MCP servers (PostgreSQL, FTP, Validation)
2. Studied MCP configuration files and server structure
3. Identified common patterns and best practices
4. Determined required dependencies and imports
5. Planned the implementation architecture
6. Researched 2025 security best practices and vulnerabilities

Key Insights from Research

• MCP servers require specific imports from the mcp package
• Configuration files must follow a specific JSON structure
• Error handling and logging are crucial for production use
• Analytics integration should be modular and optional
• Web servers need to run in separate threads to avoid blocking MCP operations
• Security is paramount in 2025 - input validation, authentication, and monitoring are essential

Implementation Strategy

Implementation Details

This section provides a detailed breakdown of how each component of MCP Help 001 was implemented.

Core Architecture

MCP Help 001 Architecture:
├── MCPHelp001Server (Main MCP Server)
│   ├── AnalyticsDB (SQLite Database Handler)
│   ├── WebServer (HTTP Server with Threading)
│   └── Tool Handlers (Browser Automation)
├── mcp.json (Cursor Configuration)
├── README.md (Documentation)
└── analytics.db (SQLite Database - Auto-created)

Database Schema

CREATE TABLE analytics (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp TEXT NOT NULL,
    action TEXT NOT NULL,
    url TEXT,
    user_agent TEXT,
    ip_address TEXT,
    session_id TEXT
);

Tool Definitions

The MCP server exposes four main tools:

Web Interface Features

• Real-time Clock: JavaScript updates time every second
• Responsive Design: Works on desktop, tablet, and mobile
• Modern Styling: Gradient backgrounds and glassmorphism effects
• Analytics Display: Shows live statistics from the database
• Auto-refresh: Button to refresh data and statistics

Key Features and Capabilities

Technical Specifications

Step-by-Step Implementation Tutorial

Follow this tutorial to create your own MCP server with similar functionality.

Step 1: Environment Setup

# Install required dependencies
pip install mcp

# Create project directory
mkdir my-mcp-server
cd my-mcp-server

Step 2: Create the MCP Server

Create a server.py file with the following structure:

#!/usr/bin/env python3
import asyncio
import json
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent

# Initialize server
server = Server("my-mcp-server")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="my_tool",
            description="Description of what the tool does",
            inputSchema={
                "type": "object",
                "properties": {},
                "required": []
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "my_tool":
        return [TextContent(type="text", text="Tool executed successfully")]
    return [TextContent(type="text", text=f"Unknown tool: {name}")]

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream, server.create_initialization_options())

if __name__ == "__main__":
    asyncio.run(main())

Step 3: Create MCP Configuration

Create a mcp.json file:

{
  "mcpServers": {
    "my-mcp-server": {
      "command": "python",
      "args": ["/path/to/your/server.py"],
      "env": {
        "PYTHONPATH": "/path/to/your/project"
      }
    }
  }
}

Step 4: Add to Cursor Configuration

Copy the contents of your mcp.json to Cursor's MCP configuration file, typically located at:

• Windows: %APPDATA%\Cursor\User\globalStorage\cursor.mcp\mcp.json
• macOS: ~/Library/Application Support/Cursor/User/globalStorage/cursor.mcp/mcp.json
• Linux: ~/.config/Cursor/User/globalStorage/cursor.mcp/mcp.json

Step 5: Test Your MCP Server

Security Considerations for 2025

As of 2025, MCP security has become a critical concern. This section covers the latest security best practices and emerging threats.

Essential Security Measures

Known Vulnerabilities (2025)

Security Best Practices

Security Implementation Checklist:

✅ Input Validation & Sanitization
✅ Principle of Least Privilege
✅ Secure Tool Registry Management
✅ Continuous Monitoring & Detection
✅ Incident Response Preparedness
✅ Regular Security Audits
✅ Data Classification & Encryption
✅ Authentication & Access Controls
✅ Request Cancellation & Timeouts
✅ Structured Logging with Correlation IDs

Creating MCP Servers with AI Assistance

This section explains how to effectively use AI assistants like ChatGPT, Claude, or Cursor's built-in AI to create custom MCP servers.

Effective AI Prompting for MCP Development

AI-Assisted Development Workflow

1. Research Phase: Ask AI to analyze existing MCP implementations and identify patterns
2. Planning Phase: Request architecture recommendations and technology choices
3. Implementation Phase: Get code generation with detailed explanations
4. Security Phase: Request security best practices and vulnerability assessments
5. Testing Phase: Request testing strategies and debugging assistance
6. Documentation Phase: Ask for comprehensive documentation and tutorials

Common AI Prompts for MCP Development

Effective Prompts for 2025:

"Create an MCP server that [specific functionality] with [specific requirements] and 2025 security best practices"

"Analyze this MCP implementation and suggest security improvements for [specific aspect]"

"Help me debug this MCP server error: [error message and code]"

"Generate a comprehensive tutorial for creating secure MCP servers with [specific features]"

"Review this MCP configuration and ensure it follows 2025 best practices and security standards"

"Assess the security vulnerabilities in this MCP implementation"