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MCP (Model Context Protocol)

Advanced⏱ 12 min read📅 Updated: 2026-02-17

🔌 Introduction – The Tool Integration Problem

AI Agents tools use pannanum. But every AI model has its own way of connecting tools! 😫

The Problem:

OpenAI: Function calling format A
Anthropic: Tool use format B
Google: Function declarations format C
Open source: Various formats D, E, F...

Oru tool build pannaa, every model ku separately integrate pannanum. Nightmare! 🤯

The Solution: MCP (Model Context Protocol) 🎉

MCP = Universal standard for connecting AI models to tools and data.

Analogy:

Before USB: Every device had different cable 😤
After USB: One cable for everything! 😊

Before MCP: Every model different tool format

After MCP: One protocol for all models! 🔌

🧠 What MCP Actually Does

MCP three main things provide pannum:

1. Tools 🔧

External functions agents call pannalaam
Search, calculate, send email, query DB
Standardized input/output format

2. Resources 📚

Data sources agents read pannalaam
Files, databases, API endpoints
Like giving agent a library card

3. Prompts 📝

Reusable prompt templates
Pre-built instructions for specific tasks
Shareable across applications

Component	What	Example
Tools	Actions to execute	send_email(), search_web()
Resources	Data to read	user_profile, company_docs
Prompts	Templates to use	"Summarize this document"

MCP combines all three into one clean protocol! ✨

🏗️ MCP Architecture

🏗️ Architecture Diagram

```
┌─────────────────────────────────────────────┐
│              MCP ARCHITECTURE                │
│                                              │
│  ┌──────────────────────────────────────┐   │
│  │         MCP CLIENT (Host)            │   │
│  │  Claude Desktop / Cursor / Your App  │   │
│  │  ┌──────────┐  ┌──────────────────┐ │   │
│  │  │ AI Model │  │ MCP Client SDK   │ │   │
│  │  │ (LLM)   │  │ (connects to     │ │   │
│  │  │         │  │  MCP servers)     │ │   │
│  │  └──────────┘  └────────┬─────────┘ │   │
│  └─────────────────────────┼───────────┘   │
│                            │                │
│              MCP Protocol (JSON-RPC)        │
│              stdio / HTTP+SSE               │
│                            │                │
│         ┌──────────────────┼────────────┐   │
│         │                  │            │   │
│         ▼                  ▼            ▼   │
│  ┌────────────┐   ┌────────────┐ ┌────────┐│
│  │ MCP Server │   │ MCP Server │ │  MCP   ││
│  │ (Weather)  │   │ (Database) │ │ Server ││
│  │            │   │            │ │(GitHub)││
│  │ 🌤️ Tools:  │   │ 💾 Tools:  │ │🐙Tools:││
│  │ get_weather│   │ query_db   │ │list_PRs││
│  │ forecast   │   │ insert_row │ │review  ││
│  └────────────┘   └────────────┘ └────────┘│
└─────────────────────────────────────────────┘

Key: Client ←→ Server communication via MCP Protocol
     One client can connect to MULTIPLE servers!
```

🔄 How MCP Works – Step by Step

MCP Communication Flow:

Step 1: Discovery 🔍

Client asks server: "What tools do you have?"

json

// Server responds:
{
  "tools": [
    {
      "name": "get_weather",
      "description": "Get weather for a city",
      "inputSchema": {
        "type": "object",
        "properties": {
          "city": {"type": "string"},
          "date": {"type": "string"}
        }
      }
    }
  ]
}

Step 2: Tool Call 🔧

Client requests tool execution:

json

{
  "method": "tools/call",
  "params": {
    "name": "get_weather",
    "arguments": {"city": "Chennai", "date": "2026-02-17"}
  }
}

Step 3: Response ✅

Server executes and returns:

json

{
  "content": [{
    "type": "text",
    "text": "Chennai: 32°C, Sunny, Humidity 65%"
  }]
}

Step 4: LLM Interpretation 🧠

AI model takes the result and crafts response for user.

Clean, simple, standard! 🎯

🆚 MCP vs Other Approaches

Feature	Function Calling	LangChain Tools	MCP
Standard	Model-specific	Framework-specific	Universal
Portability	❌ Locked to provider	❌ Locked to LangChain	✅ Any client
Discovery	Manual definition	Manual import	Auto-discovery
Transport	HTTP only	In-process	stdio / HTTP+SSE
Resources	❌ No	❌ No	✅ Built-in
Prompts	❌ No	❌ No	✅ Built-in
Security	Basic	Basic	Built-in sandboxing
Community	Platform-specific	Large but fragmented	Growing fast

MCP Advantages:

✅ Write tool once, use with any AI model
✅ Auto-discovery – no manual tool registration
✅ Resources + Prompts + Tools in one protocol
✅ Open standard – no vendor lock-in
✅ Growing ecosystem of pre-built servers

MCP = The future of AI tool integration! 🔮

🛠️ Building an MCP Server

Creating your own MCP server is surprisingly simple!

Basic MCP Server Structure:

code

my-mcp-server/
├── src/
│   └── index.ts          # Server implementation
├── package.json
└── tsconfig.json

Key Components:

Server Setup

code

const server = new McpServer({
  name: "my-tools",
  version: "1.0.0"
});

Define Tools

code

server.tool("get_stock_price", 
  { symbol: z.string() },
  async ({ symbol }) => {
    const price = await fetchStockPrice(symbol);
    return { content: [{ type: "text", text: price }] };
  }
);

Define Resources

code

server.resource("company-docs",
  "docs://company/handbook",
  async () => ({
    contents: [{ text: handbookContent }]
  })
);

Start Server

code

server.connect(new StdioTransport());

That's it! Your MCP server is ready! 🚀

🎬 Real MCP Use Case – Personal Productivity

✅ Example

Building a Productivity MCP Server:

code

MCP Server: "productivity-tools"

Tools:
├── 📧 send_email(to, subject, body)
├── 📅 create_event(title, date, time, duration)
├── ✅ add_todo(title, priority, due_date)
├── 📝 create_note(title, content, folder)
├── 🔍 search_notes(query)
└── 📊 get_daily_summary()

Resources:
├── 📋 todos://today → Today's task list
├── 📅 calendar://week → This week's events
└── 📧 emails://unread → Unread emails summary

User to Claude Desktop:

"Check my unread emails, add important ones as todos,

and block time on my calendar for follow-ups"

Claude (via MCP):

1. Reads emails://unread resource

2. Calls add_todo() for 3 important emails

3. Calls create_event() to block follow-up time

4. Responds with summary

All through standard MCP protocol! ✨

🌍 MCP Ecosystem

Growing ecosystem of MCP servers:

Category	MCP Servers	What They Do
Dev Tools	GitHub, GitLab, Linear	Code management
Database	PostgreSQL, SQLite, Supabase	Data access
Communication	Slack, Gmail, Discord	Messaging
Productivity	Notion, Google Drive, Obsidian	Knowledge mgmt
Cloud	AWS, GCP, Cloudflare	Infrastructure
Search	Brave Search, Exa	Web search
Finance	Stripe, QuickBooks	Payments
AI	Hugging Face, Replicate	Model access

MCP Clients (Hosts):

🤖 Claude Desktop
💻 Cursor IDE
📝 Zed Editor
🔧 Continue.dev
🛠️ Custom applications

Ecosystem rapidly growing – 1000+ community servers available! 📈

🔒 MCP Security Model

MCP security important – agents with tool access = power + risk!

Security Layers:

1. Transport Security 🔐

stdio: Process-level isolation
HTTP+SSE: TLS encryption
No unauthorized access

2. Permission Model 🛡️

code

Permissions:
├── Tools: Which tools can client call?
├── Resources: Which resources can client read?
├── Rate Limits: How many calls per minute?
└── Scope: What data can tools access?

3. Human-in-the-Loop 👤

Sensitive tool calls require user approval
"Agent wants to send email. Allow?" → User confirms

4. Sandboxing 📦

MCP servers run in isolated environments
Can't access host system directly
Limited file system access

Security Best Practices:

Practice	Why
Minimal permissions	Reduce attack surface
User approval for sensitive ops	Human oversight
Audit logging	Track all tool calls
Input validation	Prevent injection
Rate limiting	Prevent abuse
Regular updates	Security patches

🔄 MCP Transport Protocols

MCP two transport methods support pannum:

1. stdio (Standard I/O) 📟

code

Client ←stdin/stdout→ MCP Server Process

Server as child process run aagum
Fast, secure, local only
Best for: Desktop apps, CLI tools

2. HTTP + SSE (Server-Sent Events) 🌐

code

Client ←HTTP POST→ MCP Server (remote)
Client ←SSE stream← MCP Server (events)

Server remotely run aagum
Network-accessible
Best for: Cloud deployments, shared servers

Transport	Speed	Security	Deployment	Best For
stdio	Very fast	High (local)	Local only	Desktop apps
HTTP+SSE	Fast	Medium (needs TLS)	Anywhere	Cloud, shared

Recommendation:

Development: stdio (easy setup)
Production: HTTP+SSE (scalable)
Both: Use HTTP+SSE with proper TLS 🔒

🧪 Try It – Design an MCP Server

📋 Copy-Paste Prompt

```
Design an MCP server for a "Smart Home" system:

1. List all TOOLS the server should expose:
   - What actions can the AI take?
   - What parameters does each tool need?
   - What does each tool return?

2. List all RESOURCES:
   - What data should be readable?
   - What's the URI format?

3. List PROMPT templates:
   - What pre-built prompts would be useful?

4. Security considerations:
   - Which tools need user approval?
   - What rate limits?

Example format:
Tool: set_light(room: string, brightness: number, color: string)
  → {status: "success", new_state: {...}}

Resource: devices://living-room
  → List of all devices in living room with status

Be creative and thorough!
```

Designing MCP servers = designing agent capabilities! 🏠

💡 MCP Best Practices

💡 Tip

Building great MCP servers:

1. Clear tool descriptions – LLM reads these to decide when to use

2. Typed parameters – Use JSON Schema for validation

3. Meaningful errors – Return helpful error messages, not just "error"

4. Idempotent operations – Same call twice = same result (when possible)

5. Batch support – Allow processing multiple items at once

6. Versioning – Version your server for backward compatibility

7. Documentation – README with examples for each tool

8. Testing – Test with multiple MCP clients

Tool description quality directly impacts agent accuracy! 📝

⚠️ MCP Limitations and Challenges

⚠️ Warning

Current limitations to be aware of:

- 📈 Ecosystem still growing – Not all integrations available yet

- 🔧 Client support varies – Not all AI apps support MCP yet

- 📋 Spec evolving – Breaking changes possible

- 🐛 Debugging tools – Still maturing

- 💻 Local-first bias – stdio transport is local-only

- 📊 No built-in analytics – Need external monitoring

But these are rapidly improving! Anthropic and community actively developing. By mid-2026, most limitations will be addressed! 🚀

📝 Summary

Key Takeaways:

✅ MCP = Universal protocol for AI-tool integration (like USB for AI)

✅ Three components: Tools (actions), Resources (data), Prompts (templates)

✅ Architecture: Client (host app) ←MCP Protocol→ Servers (tools)

✅ Transports: stdio (local, fast) and HTTP+SSE (remote, scalable)

✅ Security: Permissions, human-in-the-loop, sandboxing, audit logging

✅ Ecosystem: 1000+ community servers, growing rapidly

✅ Created by Anthropic, open standard for everyone

Next article la Autonomous Agents paapom – fully self-directed AI systems! 🤖🧠

🏁 🎮 Mini Challenge

Challenge: Design Oru MCP Server

MCP protocol understand panna MCP server design:

Scenario: Weather tool share panna MCP server create

Step 1: Define Resources (3 mins)

Weather data expose panna resources:

code

weather://cities/{city}/current
weather://cities/{city}/forecast
weather://alerts/{region}

Step 2: Define Tools (4 mins)

Actions expose:

json

{
  "name": "get_weather",
  "description": "Get current weather for a city",
  "inputSchema": {
    "type": "object",
    "properties": {
      "city": {"type": "string"},
      "units": {"type": "string", "enum": ["celsius", "fahrenheit"]}
    },
    "required": ["city"]
  }
}

Step 3: Define Prompts (3 mins)

Reusable instructions:

code

"weather_summary": "Provide a brief weather summary for the given city in Tanglish"
"travel_advisory": "Give travel safety advice based on weather conditions"

Step 4: Implement Server (3 mins)

Pseudo-code:

code

MCP Server:
  - Listen on stdio/HTTP
  - Handle resource requests
  - Execute tools
  - Return structured responses

Step 5: Register & Connect (2 mins)

Register server in MCP client config
Test tool calls
Verify responses

MCP server complete! Now any MCP client (Claude, Cursor, etc.) use pannalaam! 🔌

💼 Interview Questions

Q1: MCP yaaru create pannanga, why?

A: Anthropic (Claude creators) create pannanga. Problem: Every AI model different tool formats (OpenAI function calling, Claude tools, Google, etc.). Fragmented ecosystem! Solution: One universal standard = MCP. USB analogy perfect!

Q2: MCP advantage vs function calling?

A: Function calling model-specific. OpenAI style, Anthropic style, Google style – different! MCP: One protocol, all models, all tools. Write once, use everywhere! Massive advantage for tools teams!

Q3: MCP server implement panna difficulty level?

A: Easy! Anthropic SDK simple. Python/Node.js examples available. 1 day implement panna mudiyum basic server. Community servers explore panna, fork customize panna easy!

Q4: MCP security concerns?

Tool sandboxing: Limit dangerous operations
Permissions: Users approve before execution
Human-in-the-loop: Critical actions human confirm
Audit logs: Track all tool calls
Rate limiting: Prevent abuse

Proper MCP server: All security measures built-in!

Q5: MCP future – 2027 la mainstream aaguma?

A: Yes! Already adopted: Claude, Cursor, Zed. More tools integrating daily. Community explosion ongoing. By 2027, MCP standard = industry norm. New tools MCP support pannanum illa, behind time! 🚀

❓ Frequently Asked Questions

❓ MCP na enna simple la?

Model Context Protocol – AI models ku external tools and data connect panna oru universal standard. USB port maari – any device (tool) connect pannalaam oru standard interface la.

❓ MCP yaaru create pannanga?

Anthropic (Claude creators) create pannanga. Open standard aa release pannanga so anyone can implement it. Now many companies adopting.

❓ MCP vs Function Calling – enna difference?

Function calling is model-specific (OpenAI style, Anthropic style). MCP is universal – one protocol, any model, any tool. Write once, use everywhere.

❓ MCP production-ready aa?

Rapidly maturing. Claude, Cursor, Zed already support. Community growing fast. For new projects, definitely consider MCP.

❓ MCP free aa?

Yes! Open standard, open source implementations. Anyone can build MCP servers and clients. No licensing fees.

🧠Knowledge Check

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Learning Paths

Exam Prep

MCP (Model Context Protocol)

🔌 Introduction – The Tool Integration Problem

🧠 What MCP Actually Does

🏗️ MCP Architecture

🔄 How MCP Works – Step by Step

🆚 MCP vs Other Approaches

🛠️ Building an MCP Server

🎬 Real MCP Use Case – Personal Productivity

🌍 MCP Ecosystem

🔒 MCP Security Model

🔄 MCP Transport Protocols

🧪 Try It – Design an MCP Server

💡 MCP Best Practices

⚠️ MCP Limitations and Challenges

📝 Summary

🏁 🎮 Mini Challenge

💼 Interview Questions

❓ Frequently Asked Questions