Technical
11/5/2025
10 min read

Understanding Model Context Protocol: A Technical Guide

Development Team

What is Model Context Protocol?

Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI assistants to connect to data sources and tools in a secure, consistent way.

Think of MCP as USB-C for AI integration. Instead of building custom integrations for each AI tool and data source, MCP provides one universal protocol.

Why Anthropic Created MCP

According to Anthropic's announcement:

"MCP is a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments."

The goal: replace fragmented, custom integrations with a universal standard.

How MCP Works

MCP uses a client-server architecture with three core components:

1. MCP Client (Built into AI tools)

Modern AI coding assistants have built-in MCP client support:

  • Claude Code (Desktop app)
  • Cursor IDE (Native integration)
  • VS Code (via MCP extension)
  • Windsurf, Zed, Replit, Codeium

2. MCP Server (Your integration layer)

Exposes resources and tools via standardized MCP protocol. Each server provides:

  • Resources AI can read (documentation, code examples, notes)
  • Tools AI can execute (create notes, search data, run queries)
  • Prompts (optional templates)

3. Resources (Your data and tools)

Anything your AI assistant needs access to:

  • Documentation and code examples
  • Database queries and APIs
  • Project notes and configurations
  • Custom prompt templates

The MCP Ecosystem: What's Actually Useful?

MCP is early and a lot of hype is around what's possible but not what's actually useful right now.

Here are the top 6 most useful MCP servers that developers are using daily:

1. Context7 - Up-to-Date Documentation

What it does: Provides current library documentation directly to your AI assistant.

Why it's useful:

  • AI gets current docs (not outdated training data)
  • Works with thousands of frameworks
  • Automatic version detection

2. Playwright - Browser Automation

What it does: Provides browser automation capabilities for AI assistants using Playwright.

Why it's useful:

  • AI can interact with web pages, take screenshots, and scrape data
  • Generate and run end-to-end test code automatically
  • Uses accessibility tree (structured data) instead of visual models
  • Automate web workflows and extract information from real browsers

3. Sentry - Error Context Integration

What it does: Connects AI to your Sentry error tracking system to retrieve full issue context.

Why it's useful:

  • AI reads error stack traces, user context, and breadcrumbs directly from Sentry
  • Access to 16+ Sentry tools and prompts for comprehensive error analysis
  • Trigger Seer Analysis for AI-powered fix recommendations
  • Debug production issues faster with complete error context

4. GitHub - Repository Management

What it does: GitHub's official MCP server enables AI to interact with repositories, issues, and workflows.

Why it's useful:

  • Browse code, search files, and analyze commits
  • Create and manage issues and pull requests automatically
  • Monitor GitHub Actions workflows and analyze build failures
  • Review security findings and Dependabot alerts
  • Understand project structure without manual exploration

5. PostgreSQL - Database Schema Access

What it does: Provides read-only access to PostgreSQL databases for AI assistants.

Why it's useful:

  • AI can inspect database schemas and table structures
  • Execute read-only SQL queries to understand your data
  • Get column names, data types, and relationships automatically
  • Build features with accurate database context
  • Note: Use with development databases only, not production

6. Noteit MCP - Agent Profiles & Development Notes

What it does: Composable AI agent profiles and structured note system for developers.

Why it's useful:

  • Agent Profiles: Mix personas, instructions, rules, and documents to configure AI behavior
  • 60+ Templates: Pre-built profiles for different roles (Code Reviewer, Debug Assistant, etc.)
  • Development Notes: Record decisions, tasks, and explanations with Mermaid diagram support
  • On-Demand Configuration: Call any profile anytime during your coding workflow

Setup:

{
  "mcpServers": {
    "noteit-mcp": {
      "type": "http",
      "url": "https://www.noteit-mcp.com/api/mcp"
    }
  }
}

Getting Started with MCP

Step 1: Choose Your AI Tool

  • Claude Code (recommended for beginners)
  • Cursor IDE (best developer experience)
  • VS Code + MCP extension

Step 2: Add Useful MCP Servers Start with these 3:

  • Context7 (documentation)
  • Noteit MCP (workflow management)
  • One specific to your stack (GitHub, PostgreSQL, etc.)

Step 3: Test with Real Tasks

  • Code review
  • Bug debugging
  • Documentation lookup

Conclusion

Model Context Protocol is transforming how developers work with AI coding assistants:

✅ Universal Standard: One protocol, any AI tool ✅ Secure by Design: OAuth 2.1 + PKCE authentication ✅ Context-Aware: AI remembers your project specifics ✅ Ecosystem Growing: Major IDE and enterprise adoption ✅ Developer-Friendly: Easy to implement and extend

Ready to try MCP?

Get Started →

Tags

MCP
Model Context Protocol
AI Coding
Claude Code