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Bhavesh Parakh
Bhavesh Parakh

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The Complete Guide to Model Context Protocol (MCP): From Confusion to Clarity

#mcp #ai #tutorial #explainlikeimfive

A comprehensive deep-dive into MCP based on real developer questions and practical examples

Table of Contents

  1. What is MCP Really?
  2. The Core Problem MCP Solves
  3. Where is the LLM Actually Located?
  4. MCP vs React Agents: The Real Difference
  5. Current Platform Support
  6. Practical Implementation Guide
  7. Real Use Cases for Developers
  8. When MCP Makes Sense (And When It Doesn't)
  9. The Honest Truth About MCP

What is MCP Really?

Let's start with the simplest analogy that cuts through all the confusion:

MCP is like USB-C for AI applications.

Just as USB-C provides a standardized way to connect your devices to various peripherals, MCP provides a standardized way to connect AI models to different data sources and tools.

The USB Analogy

Before USB existed:

  • Want to connect a mouse? Need a mouse port
  • Want to connect a keyboard? Need a keyboard port
  • Want to connect a printer? Need a printer port
  • Every device needed its own special port and cable!

After USB was invented:

  • One USB port connects ANY device
  • Mouse, keyboard, printer, speakers - all use the same USB port

Before MCP existed:

  • Want Claude to read Gmail? Need Claude-Gmail code
  • Want Claude to read Slack? Need Claude-Slack code
  • Want GPT to read Gmail? Need GPT-Gmail code (different from Claude!)
  • Every AI model needed special code for every app!

After MCP was invented:

  • One MCP connection lets ANY AI model connect to ANY app
  • Claude, GPT, Gemini - all use the same MCP connection
  • Gmail, Slack, GitHub - all work with the same MCP standard

The Core Problem MCP Solves

The M×N Integration Nightmare

The industry faced what's called the "M×N problem":

  • If you have M different AI models and N different services
  • You need to build M×N different integrations

Real Example:

  • 3 AI models (Claude, GPT-4, Gemini)
  • 4 services (GitHub, Google Calendar, PostgreSQL, Slack)
  • Without MCP: 3×4 = 12 custom integrations needed
  • With MCP: 3+4 = 7 standardized components

The Mathematical Impact

Scenario Without MCP With MCP Savings
3 models, 2 tools 6 functions 5 components 17%
3 models, 5 tools 15 functions 8 components 47%
5 models, 5 tools 25 functions 10 components 60%
10 models, 10 tools 100 functions 20 components 80%

As you scale up, MCP saves exponentially more work!

Where is the LLM Actually Located?

This was one of the biggest sources of confusion in our discussion. Let me clarify exactly where the LLM lives in the MCP ecosystem.

What YOU Write (No LLM)

1. Your FastAPI App (main.py):

# FastAPI - NO LLM HERE
@app.get("/weather/{city}")
def get_weather_api(city: str):
    response = requests.get(f"https://api.openweathermap.org/weather?q={city}")
    return {"city": city, "temperature": 72, "conditions": "sunny"}

# Just API calls and data processing - NO AI
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2. Your MCP Server (weather_server.py):

# MCP Server - NO LLM HERE EITHER
@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_weather":
        response = requests.get(f"http://localhost:8000/weather/{city}")
        return [TextContent(type="text", text=response.text)]

# Just tool definitions and API calls - NO AI
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What AI Companies Provide (Has LLM)

3. Claude Desktop App (Anthropic writes this):

# THIS CODE IS INSIDE CLAUDE DESKTOP - YOU NEVER SEE IT
class ClaudeDesktop:
    def __init__(self):
        self.llm = AnthropicClaudeModel()  # ← HERE IS THE LLM!
        self.mcp_client = MCPClient()
        self.chat_interface = ChatUI()

    def process_user_message(self, user_input: str):
        # LLM processes the user input
        llm_response = self.llm.analyze(user_input)  # ← LLM THINKING!

        if llm_response.should_use_tool:
            tool_result = self.mcp_client.call_tool(...)
            final_response = self.llm.format_response(tool_result)  # ← MORE LLM!
            return final_response
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The Complete Data Flow 

USER: "What's the weather in New York?"

┌─────────────────────────────────────┐
│ Claude Desktop App                  │
│ ┌─────────────────────────────────┐ │
│ │ 🧠 LLM (Claude AI Model)        │ │ ← LLM IS HERE!
│ │ Analyzes: "User wants weather   │ │
│ │ for New York. I should use the  │ │
│ │ get_weather tool."              │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────┘
         │
         │ MCP call: get_weather("New York")
         ▼
┌─────────────────────────────────────┐
│ Your MCP Server (weather_server.py)│
│ NO LLM - just calls your FastAPI   │
└─────────────────────────────────────┘
         │
         │ HTTP call: GET /weather/New York
         ▼
┌─────────────────────────────────────┐
│ Your FastAPI (main.py)              │
│ NO LLM - just calls OpenWeather API│
│ Returns: {"temp": 72, ...}          │
└─────────────────────────────────────┘
         │
         │ Weather data flows back up
         ▼
┌─────────────────────────────────────┐
│ Claude Desktop App                  │
│ ┌─────────────────────────────────┐ │
│ │ 🧠 LLM (Claude AI Model)        │ │ ← LLM AGAIN!
│ │ Takes raw data: {"temp": 72}    │ │
│ │ Formats: "The weather in New    │ │
│ │ York is 72°F and sunny!"        │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────┘
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Key Insight: The LLM is in the AI application (Claude Desktop/Cursor), not in your code!

MCP vs React Agents: The Real Difference

This comparison was crucial to understanding MCP's unique value proposition.

React Agent Approach 

# YOU build the entire AI system
from langchain.agents import initialize_agent, Tool, AgentType
from langchain.llms import OpenAI

# YOU provide the LLM
llm = OpenAI(api_key="your-openai-key")

# YOU create the agent
agent = initialize_agent(tools=tools, llm=llm, agent=AgentType.REACT)

# YOU run the conversation
result = agent.run("What's the weather?")
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What YOU provide:

  • ✋ OpenAI API key & pay for LLM calls
  • ✋ Agent creation code
  • ✋ Conversation interface
  • ✋ User input/output handling

MCP Approach 

# YOU just provide tools, AI system already exists
@server.list_tools()
async def list_tools():
    return [Tool(name="get_weather", description="Get weather")]

@server.call_tool() 
async def call_tool(name: str, arguments: dict):
    return [TextContent(type="text", text="Weather data")]
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What CLAUDE provides:

  • 🏢 LLM (you don't pay extra)
  • 🏢 Agent intelligence
  • 🏢 Chat interface
  • 🏢 User input/output handling

Side-by-Side Comparison

Aspect React Agent MCP
LLM Management YOU provide & manage LLM AI app provides LLM
Agent Creation YOU create agent AI app has built-in agent
User Interface YOU build chat UI AI app provides UI
Code Complexity High (100+ lines) Low (20-30 lines)
Multi-Model Support Rebuild for each LLM Works with any MCP client
Maintenance YOU handle everything AI companies handle infrastructure

Analogy:

  • React Agent = Building your own car from scratch
  • MCP = Providing GPS to existing cars

Current Platform Support

Understanding which platforms actually support MCP was critical for practical implementation.

MCP Support Status (2025)

Platform Status Ready to Use? Notes
Claude Desktop ✅ FULL SUPPORT Yes, since Nov 2024 Production ready
Cursor IDE ✅ FULL SUPPORT Yes, since Jan 2025 Production ready
VS Code ✅ VIA EXTENSIONS Yes, with MCP extensions Community extensions
ChatGPT/OpenAI ❌ NOT YET No, but planned for 2025 Officially announced
Google Gemini ❌ NOT YET No official announcement No timeline given
GitHub Copilot 🔄 IN PROGRESS Beta testing Limited availability

Important Reality Check

For Cursor IDE specifically:

  • ✅ Cursor's Claude model: HAS MCP support
  • ❌ Cursor's GPT-4 model: NO MCP support
  • ❌ Cursor's GPT-3.5 model: NO MCP support

What this means:

When Claude is selected in Cursor:
You: "What tools do you have?"
Claude: "I have access to: weather, calendar, database tools"

When GPT-4 is selected in Cursor:  
You: "What tools do you have?"
GPT-4: "I don't have access to any external tools"
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Practical Implementation Guide

Step-by-Step Setup

1. Create Your MCP Server

File: weather_server.py

from mcp.server import Server
from mcp.types import Tool, TextContent
import json
import requests

server = Server("weather-server")

@server.list_tools()
async def list_tools():
    """MCP calls this automatically when client asks for available tools"""
    return [
        Tool(
            name="get_weather",
            description="Get current weather for a location",
            inputSchema={
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"}
                },
                "required": ["location"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    """MCP calls this automatically when client wants to use a tool"""
    if name == "get_weather":
        location = arguments["location"]

        # Call real weather API
        api_key = "your-openweather-api-key"
        response = requests.get(
            f"https://api.openweathermap.org/data/2.5/weather?q={location}&appid={api_key}"
        )

        if response.status_code == 200:
            data = response.json()
            weather_info = {
                "location": data["name"],
                "temperature": data["main"]["temp"],
                "conditions": data["weather"][0]["description"],
                "humidity": data["main"]["humidity"]
            }
            return [TextContent(type="text", text=json.dumps(weather_info))]
        else:
            return [TextContent(type="text", text=f"Error getting weather for {location}")]

if __name__ == "__main__":
    import asyncio
    import mcp.server.stdio

    async def main():
        async with mcp.server.stdio.stdio_server() as (read, write):
            await server.run(read, write, server.create_initialization_options())

    asyncio.run(main())
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2. Create Configuration Files

For Claude Desktop:

Location: ~/Library/Application Support/Claude/claude_desktop_config.json (Mac)

Location: %APPDATA%\Claude\claude_desktop_config.json (Windows)

{
  "mcpServers": {
    "weather": {
      "command": "python",
      "args": ["weather_server.py"]
    }
  }
}
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For Cursor IDE:

Location: .cursor/mcp_config.json (in your project root)

{
  "mcp": {
    "servers": {
      "weather": {
        "command": ["python", "weather_server.py"]
      }
    }
  }
}
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3. Understanding list_tools() and call_tool()

Important clarification: You DON'T manually call these functions. The MCP framework calls them automatically.

# ❌ WRONG WAY (Don't do this)
tools = server.list_tools()  # Don't manually call
result = server.call_tool("get_weather", {"location": "NYC"})  # Don't manually call

# ✅ RIGHT WAY (Just define them)
@server.list_tools()
async def list_tools():
    # MCP calls this automatically when client asks for available tools
    return [Tool(...)]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    # MCP calls this automatically when client wants to use a tool
    return [TextContent(...)]
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The Flow:

  1. You start: python weather_server.py
  2. Claude connects via config
  3. Claude asks: "What tools?" → MCP automatically calls list_tools()
  4. User asks: "Weather in NYC?" → MCP automatically calls call_tool("get_weather", {"location": "NYC"})

4. Local vs Remote Server Configuration

Local Server Config:

{
  "mcpServers": {
    "weather": {
      "command": "python",
      "args": ["weather_server.py"],
      "cwd": "/path/to/your/project"
    }
  }
}
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Remote Server Config:

{
  "mcpServers": {
    "weather": {
      "transport": {
        "type": "http",
        "url": "https://your-server.com/mcp"
      },
      "auth": {
        "type": "bearer",
        "token": "your-auth-token"
      }
    }
  }
}
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Docker Container Config:

{
  "mcpServers": {
    "weather": {
      "command": "docker",
      "args": ["run", "-i", "your-weather-server:latest"]
    }
  }
}
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5. Testing Your Setup

  1. Install dependencies: pip install mcp
  2. Start your server: python weather_server.py
  3. Start Claude Desktop (it reads config automatically)
  4. Test in chat: "What tools do you have?"
  5. Use your tool: "What's the weather in New York?"

What is Cursor Chat?

Cursor Chat is the AI chat panel built into Cursor IDE:

  • Access: Press Cmd+L (Mac) or Ctrl+L (Windows)
  • Location: Opens on the right side of the IDE
  • Features: Chat with AI models (GPT-4, Claude, etc.) that can see your code
  • MCP Integration: When using Claude model, can access your MCP tools

What it looks like:

┌─────────────────────────────────────────────────────────────┐
│ Cursor IDE                                    [- □ ×]       │
├─────────────────────────────────────────────────────────────┤
│ Explorer             │  main.py                │ Chat       │
│ ├── src/             │                         │            │
│ ├── main.py          │  from fastapi import... │ You:       │
│ ├── weather.py       │                         │ What       │
│ └── config.json      │  app = FastAPI()        │ tools do   │
│                      │                         │ I have?    │
│                      │  @app.get("/weather")   │            │
│                      │  def get_weather():     │ AI:        │
│                      │    return {...}         │ I have     │
│                      │                         │ access to: │
│                      │                         │ - weather  │
│                      │                         │ - calendar │
└─────────────────────────────────────────────────────────────┘
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Real Use Cases for Developers

Through our discussion, we identified several practical scenarios where MCP provides genuine value.

1. Development Workflow Enhancement

Scenario: Give AI assistants access to YOUR development environment.

# dev_tools_server.py
@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "run_tests":
        # Run YOUR project tests
        result = subprocess.run(["pytest", "tests/"], capture_output=True, text=True)
        return [TextContent(type="text", text=f"Test Results:\n{result.stdout}")]

    elif name == "query_database":
        # Query YOUR database
        query = arguments["query"]
        conn = sqlite3.connect("your_app.db")
        cursor = conn.execute(query)
        results = cursor.fetchall()
        return [TextContent(type="text", text=json.dumps(results))]

    elif name == "check_logs":
        # Read YOUR application logs
        with open("app.log", "r") as f:
            recent_logs = f.readlines()[-50:]  # Last 50 lines
        return [TextContent(type="text", text="".join(recent_logs))]
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Real Conversation:

You: "My API is slow. Can you help debug it?"

AI: [Uses MCP to check your logs] 
"I see 500ms+ response times. Let me check your database."

AI: [Uses MCP to query your database]
"Your users table has 1M records with no index on email. That's the bottleneck."

You: "Can you run the tests after I add the index?"

AI: [Uses MCP to run your tests]
"All 47 tests passed! Performance improved to 50ms average."
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2. API Integration Assistant

Scenario: AI helps debug YOUR actual API integrations.

# api_integration_server.py
@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "test_stripe_api":
        # Test YOUR Stripe integration
        stripe_key = os.getenv("STRIPE_SECRET_KEY")
        response = requests.get("https://api.stripe.com/v1/customers", auth=(stripe_key, ""))
        return [TextContent(type="text", text=f"Stripe API: {response.status_code}\n{response.text}")]

    elif name == "test_aws_s3":
        # Test YOUR AWS integration
        import boto3
        s3 = boto3.client('s3')
        try:
            buckets = s3.list_buckets()
            return [TextContent(type="text", text=f"S3 Buckets: {json.dumps(buckets, indent=2)}")]
        except Exception as e:
            return [TextContent(type="text", text=f"S3 Error: {str(e)}")]
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Real Debugging:

You: "My Stripe webhook isn't working. Can you help?"

AI: [Uses MCP to test Stripe API]
"Your Stripe API is responding correctly. Let me test your webhook endpoint."

AI: [Uses MCP to validate webhook]
"Your webhook is returning 500 error. The issue is in your signature validation."
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3. Database Administration Helper

Scenario: AI analyzes YOUR actual database and suggests optimizations.

# database_helper_server.py
@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "analyze_schema":
        # Analyze YOUR database schema
        conn = sqlite3.connect("your_app.db")
        tables = conn.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
        schema_info = {}
        for table in tables:
            table_name = table[0]
            columns = conn.execute(f"PRAGMA table_info({table_name})").fetchall()
            schema_info[table_name] = columns
        return [TextContent(type="text", text=json.dumps(schema_info, indent=2))]

    elif name == "suggest_indexes":
        # Analyze YOUR query patterns and suggest indexes
        with open("slow_queries.log", "r") as f:
            queries = f.readlines()
        # AI can now suggest specific indexes for YOUR queries
        return [TextContent(type="text", text="Index suggestions based on your query patterns")]
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4. Enhanced LLM Calls in Your Applications

Important clarification: This is where MCP can help with LLM calls you make in your own code.

# Your enhanced application
@app.post("/analyze-feedback")
def analyze_feedback(feedback_text: str, customer_id: str):
    # Get rich context from YOUR systems (this is where MCP helps)
    customer_history = get_customer_purchase_history(customer_id)
    product_info = get_product_details_from_db(product_id)
    recent_trends = analyze_recent_feedback_trends()

    # Enhanced LLM call with YOUR business context
    enhanced_prompt = f"""
    Analyze this customer feedback with full business context:

    Feedback: {feedback_text}
    Customer Tier: {customer_history.get('tier', 'Standard')}
    Product: {product_info['name']}
    Recent Trends: {recent_trends}

    Provide analysis with urgency level and recommended actions.
    """

    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": enhanced_prompt}]
    )

    return {"analysis": response.choices[0].message.content}
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The MCP benefit: External AI assistants (Claude Desktop, Cursor) can help you improve these LLM calls by analyzing your actual data patterns and suggesting better prompts.

5. ROI Calculation for Developers

Time Savings Per Week:

  • Debugging issues: 8 hours → 2 hours = 6 hours saved
  • Code review process: 4 hours → 1 hour = 3 hours saved
  • Database optimization: 6 hours → 1 hour = 5 hours saved
  • API integration debugging: 4 hours → 1 hour = 3 hours saved
  • Deployment troubleshooting: 3 hours → 30 min = 2.5 hours saved

Total: 19.5 hours saved per week = 2.4 extra days for actual development!

Setup Cost: 6 hours (learning + setup)

Payback: After 1 week of use!

When MCP Makes Sense (And When It Doesn't)

Our discussion revealed important distinctions about when MCP provides real value versus when it's overkill.

When MCP Makes Sense ✅

1. Unique Business Intelligence

You have proprietary algorithms, models, or business logic that external AIs can't replicate.

Example:

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "analyze_customer_sentiment":
        customer_id = arguments["customer_id"]

        # YOUR proprietary analysis (years of development)
        customer_history = get_full_customer_journey(customer_id)
        behavioral_patterns = analyze_customer_behavior(customer_id)
        sentiment_model = load_your_trained_sentiment_model()

        # YOUR business rules and logic
        sentiment_score = sentiment_model.predict(customer_history, behavioral_patterns)
        risk_assessment = calculate_churn_risk(customer_id, sentiment_score)

        return [TextContent(type="text", text=json.dumps({
            "sentiment_score": sentiment_score,
            "churn_risk": risk_assessment,
            "recommended_actions": generate_retention_strategy(customer_id, risk_assessment)
        }))]
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2. Exclusive Data Access

You have data that only you can access (internal systems, partnerships).

3. Cross-Platform Tool Sharing

You want your tools to work with multiple AI platforms (Claude today, GPT tomorrow, etc.).

4. External AI Client Integration

You want external AI assistants (Claude Desktop, Cursor) to use your capabilities.

When MCP Doesn't Make Sense ❌

1. Simple Data Retrieval 

# This is just data retrieval - MCP is overkill
@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_customer_data":
        # This is just a database query
        result = db.execute("SELECT * FROM customers WHERE id=?", [customer_id])
        return [TextContent(type="text", text=json.dumps(result.fetchall()))]

# Better approach: Just query directly in your app
customer_data = db.execute("SELECT * FROM customers WHERE id=?", [customer_id]).fetchone()
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2. Building Your Own AI Application

If you're building your own AI app, use React Agent or direct LLM calls.

3. Internal-Only Tools

If only your application will use the tools, MCP adds unnecessary complexity.

4. Simple CRUD Operations

Just build a regular API instead.

The Honest Truth About MCP

Based on our deep discussion, here's the unvarnished reality:

What Most "MCP Examples" Actually Are

The Problem: Most examples you see online are just:

  • ❌ Simple data retrieval dressed up as MCP
  • ❌ Adding unnecessary complexity
  • ❌ Not showing real MCP value

Example of fake value:

# This is NOT really valuable MCP usage
@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_weather":
        # This is just an API call - you could do this directly
        response = requests.get(f"https://api.weather.com/weather?q={location}")
        return [TextContent(type="text", text=response.text)]
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The Real Value Proposition

MCP's genuine value comes from:

  1. Sharing unique capabilities across the AI ecosystem
  2. Making YOUR expertise available to external AI assistants
  3. Future-proofing your tools for upcoming AI platforms
  4. Enabling AI assistants to help with YOUR specific development workflow

Practical Decision Framework

Use MCP when:

  • ✅ You have unique business logic others can't replicate
  • ✅ You want external AI assistants to use your tools
  • ✅ You're building tools for the broader AI ecosystem

Don't use MCP when:

  • ❌ Just getting data from your own database
  • ❌ Building internal-only applications
  • ❌ Simple data processing tasks

For most developers building their own apps: React Agent or direct LLM calls are simpler and better.

The Current Reality (2025)

What works today:

  • Claude Desktop + MCP = ✅ Production ready
  • Cursor IDE + MCP (Claude model) = ✅ Production ready

What's coming:

  • ChatGPT + MCP = 🔄 Announced for 2025
  • Gemini + MCP = ❓ No official timeline

Strategic Approach:

  1. Build MCP servers now for unique capabilities
  2. Use with Claude/Cursor for immediate value
  3. Benefit automatically when other platforms add support

Key Takeaways

After our comprehensive discussion, here are the essential points every developer should understand:

1. MCP Is Not Magic

It's a protocol for connecting AI assistants to tools. The value comes from the tools you build, not from MCP itself.

2. Know Where the LLM Lives

  • Your code: No LLM (just data and business logic)
  • AI applications: Have the LLM built-in (Claude Desktop, Cursor)
  • MCP: Just the connection protocol between them

3. Choose the Right Tool for the Job

  • Your own app with LLM calls: Direct data access or React Agent
  • Tools for AI ecosystem: MCP
  • Simple data retrieval: Regular API

4. Platform Support Matters

Only Claude Desktop and Cursor (Claude model) fully support MCP today. Plan accordingly.

5. Focus on Unique Value

MCP shines when you have something unique to offer the AI ecosystem, not for simple data operations.

6. Start Simple

Don't over-engineer. Build MCP servers for genuinely unique capabilities, use direct approaches for everything else.

Conclusion

Model Context Protocol represents a significant step toward a more connected AI ecosystem. However, like any technology, its value depends entirely on how and when you use it.

The key insight from our discussion is that MCP is not about replacing your existing architecture or making your LLM calls more complex. Instead, it's about sharing your unique capabilities with the broader AI ecosystem and enabling external AI assistants to become more powerful through access to your specialized tools.

As the AI landscape continues to evolve and more platforms adopt MCP, the developers who understand its true purpose—and more importantly, when not to use it—will be best positioned to benefit from this new paradigm.

Remember: The goal isn't to use MCP because it exists, but to use it when it genuinely solves a problem that simpler approaches cannot. In most cases, especially for internal applications, simpler is better.

This guide was created based on a comprehensive discussion covering every aspect of MCP, from basic concepts to advanced implementation strategies. It reflects real developer questions and practical concerns about adopting this new technology.

Top comments (1)

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dynumo profile image
Adam McBride •

What you are perhaps missing - along with many - is that there are now 3 MCP standards lol

There is the one you discuss above, using stdio for local MCP deployments, but then there is also SSE MCP and HTTP (OpenAPI) MCP, which are typically deployed on servers.

And a complete chaos of who supports which one... You can convert SSE and STDIO both to HTTP quite easily with MCPO, but I expect a lot of people to get caught out not securing their MCP endpoints.