What is MCP? A Developer's Guide to Model Context Protocol

If you've been using AI coding assistants like Claude, Cursor, or Windsurf, you've probably noticed something: they're incredibly powerful, but they live in a bubble. They can't access your project management tools, they can't read your documentation, and they forget everything between sessions.

Model Context Protocol (MCP) is changing that. It's an open protocol that lets AI assistants securely connect to external data sources and tools — including project management systems like Erold.

What Problem Does MCP Solve?

Before MCP, connecting AI assistants to external tools was a mess. Every integration was custom-built, insecure, or required exposing API keys directly to the AI. There was no standard way to:

• Give AI read access to specific data
• Let AI execute actions with proper permissions
• Maintain security boundaries
• Provide consistent context across different AI tools

MCP solves this by providing a standardized protocol. Think of it like USB for AI — a universal way to connect AI assistants to data and tools.

How MCP Works

MCP uses a client-server architecture:

1. MCP Server: A service (like Erold) that exposes data and actions via the MCP protocol
2. MCP Client: An AI assistant (like Claude) that connects to MCP servers
3. Protocol: JSON-RPC over stdio or HTTP, defining how data and actions are exposed

When you connect Claude Code to Erold's MCP server, Claude can:

• List your projects and tasks
• Read task details and comments
• Create new tasks
• Update task status
• Access your Knowledge Base

All through a secure, permission-controlled connection.

MCP vs Traditional API Integration

Why MCP Matters for Developers

If you're using AI assistants for coding, MCP unlocks a new level of productivity:

1. Zero Context Switching

Instead of copy-pasting task descriptions into your AI chat, the AI can read them directly. Ask "What's my next task?" and get an answer without leaving your editor.

2. Persistent Memory

Store important context in a Knowledge Base that your AI can access across sessions. Project architecture, coding standards, common issues — all available to your AI instantly.

3. Automated Updates

Let your AI update task status as you work. "Mark the authentication task as done" actually updates your project management tool.

4. Multi-AI Support

Use the same MCP connection whether you're using Claude, Cursor, Windsurf, or another MCP-compatible tool. One setup, all your AIs.

Getting Started with MCP

Ready to try MCP? Here's how to connect Claude Code to Erold:

# 1. Install the Erold MCP server
npx @erold/mcp-server

# 2. Get your API key from Erold
# Settings → API Keys → Create Key

# 3. Configure Claude Code
# Add to your MCP settings:
{
  "erold": {
    "command": "npx",
    "args": ["@erold/mcp-server"],
    "env": {
      "EROLD_API_KEY": "your-api-key"
    }
  }
}

That's it! Claude Code can now access your Erold tasks and Knowledge Base directly.

The Future of AI-Tool Integration

MCP is still early, but adoption is growing fast. More AI assistants are adding MCP support, and more tools are building MCP servers. We believe this is the future of how AI will interact with the software we use every day.

At Erold, we've been building with MCP in mind from day one. Our MCP server provides 27+ tools for task management, knowledge base access, and activity tracking — all through the standard MCP protocol.