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Understanding MCPs

MCPs are the UI for AI agents. Just like humans need buttons and forms, AI agents need MCPs to understand your tools.

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The Problem

Your tools work great for humans and developers:
  • Humans use buttons, forms, and visual interfaces
  • Developers use APIs with documentation
  • AI agents get confused without proper guidance
Without MCPs, AI is like a user on a webpage with 1000 random buttons scattered everywhere. They don’t know:
  • Which button does what
  • When to click which button
  • What happens after clicking

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What MCPs Provide

MCPs solve this by giving AI three things:

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1. Tools (Actions)

What AI can DO with your system: 
{
  "name": "send_email",
  "description": "Send an email to someone", 
  "parameters": {
    "to": "email address",
    "subject": "email subject",
    "body": "email content"
  }
}

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2. Resources (Data)

What AI can ACCESS from your system: 
{
  "uri": "user://contacts",
  "name": "User Contacts",
  "description": "List of user contact information"
}

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3. Prompts (Templates)

What AI can USE as starting points: 
{
  "name": "meeting_invite",
  "description": "Create a meeting invite email",
  "template": "Hi {name}, let's meet on {date} at {time}..."
}

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Why This Matters

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For AI Agents

  • Clear guidance - Know exactly what tools are available
  • Better decisions - Understand when to use each tool
  • Fewer errors - Proper schemas prevent mistakes

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For You

  • Better results - AI uses your tools correctly
  • Less confusion - AI doesn’t make random tool calls
  • More adoption - People use tools that work well

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Real Example

Without MCP: 
User: "Send John a meeting invite"
AI: "I don't know how to send emails or who John is"
With MCP: 
User: "Send John a meeting invite"
AI: "I'll check your contacts for John and send him a meeting invite"
   → Uses contact resource to find John
   → Uses email tool to send invite
   → Uses meeting template for content

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MCP vs API

APIMCP
For developersFor AI agents
Static documentationDynamic discovery
Human interpretationAI interpretation
Complex integrationSimple integration

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The Art of Good MCPs

Building good MCPs is like designing good user interfaces:

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❌ Bad UI

  • 1000 buttons everywhere
  • Unclear labels
  • No organization
  • Confusing workflows

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✅ Good UI

  • Clear, organized sections
  • Descriptive labels
  • Logical grouping
  • Intuitive flow

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❌ Bad MCP

  • Too many similar tools
  • Vague descriptions
  • Complex schemas
  • Overlapping functionality

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✅ Good MCP

  • Focused tool set
  • Clear descriptions
  • Simple schemas
  • Distinct purposes

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Next Steps

Best Practices

Learn how to design MCPs that AI loves

Testing Guide

Test your MCPs with real AI agents

Quick Start

Build your first MCP in 5 minutes

API Reference

Explore the LeanMCP API

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Key Takeaway

MCPs are not just technical specifications - they’re the interface between your tools and AI intelligence. The better your MCP, the better AI can help your users accomplish their goals.