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Tandap Noel Bansikah
Tandap Noel Bansikah

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MCP Basics - Understanding the Model Context Protocol

#mcp #ai #programming #productivity

MCP Basics - Understanding the Model Context Protocol

A beginner-friendly introduction to the Model Context Protocol (MCP) and its core concepts.

📚 Part of the AI & MCP Toolkit - A comprehensive collection of MCP servers, learning resources, and development tools.

What is MCP?

Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to external data sources and tools. Think of it as a bridge between AI models and the real world.

🔗 Learn More: Official MCP Specification | MCP Python SDK

Why MCP Matters

Before MCP

  • AI assistants were isolated from external data
  • Limited to pre-trained knowledge
  • Couldn't perform real-world actions
  • Required custom integrations for each use case

With MCP

  • AI assistants can access live data
  • Perform actions through standardized tools
  • Connect to databases, APIs, file systems, and more
  • Secure, controlled access to resources

Core Concepts

1. Servers and Clients 

┌─────────────┐    MCP Protocol    ┌─────────────┐
│   Client    │ ◄─────────────────► │   Server    │
│ (AI Agent)  │                    │ (Data/Tools)│
└─────────────┘                    └─────────────┘
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  • Client: The AI assistant (like Claude, Kiro, etc.)
  • Server: Provides tools and resources to the client
  • Protocol: JSON-RPC based communication

2. Tools

Tools are functions that clients can call to perform actions:

{
  "name": "get_weather",
  "description": "Get current weather for a location",
  "inputSchema": {
    "type": "object",
    "properties": {
      "location": {"type": "string"}
    }
  }
}
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Examples of tools:

  • Database queries
  • File operations
  • API calls
  • Calculations
  • System commands

3. Resources

Resources are data that clients can read:

{
  "uri": "file://documents/report.pdf",
  "name": "Monthly Report",
  "description": "Sales report for January",
  "mimeType": "application/pdf"
}
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Examples of resources:

  • Files and documents
  • Database records
  • API responses
  • Configuration data
  • Live data feeds

4. Communication Flow 

1. Client connects to Server
2. Client lists available tools/resources
3. Client calls tools or reads resources
4. Server processes requests and returns results
5. Client uses results to help the user
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MCP Architecture

Transport Layer

  • stdio: Standard input/output (most common)
  • HTTP: Web-based communication
  • WebSocket: Real-time bidirectional communication

Message Types

  • Requests: Client asks server to do something
  • Responses: Server replies with results
  • Notifications: One-way messages (no response expected)

Security Model

  • Servers run in isolated processes
  • Clients control which tools to expose
  • Input validation and sanitization
  • Permission-based access control

Real-World Example

Let's say you want an AI assistant to help with your project management:

1. The Problem

You need to check project status, update tasks, and generate reports.

2. The MCP Solution

Create an MCP server that connects to your project management system:

# Tools the server provides
tools = [
    "list_projects",      # Get all projects
    "get_project_status", # Check project progress
    "update_task",        # Modify task details
    "generate_report"     # Create status reports
]

# Resources the server exposes
resources = [
    "project://active-projects",    # List of active projects
    "project://team-members",       # Team information
    "project://recent-updates"      # Latest changes
]
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3. The Interaction 

User: "What's the status of Project Alpha?"

AI Assistant:
1. Calls get_project_status("Project Alpha")
2. Receives current status data
3. Responds: "Project Alpha is 75% complete, 
   with 3 tasks remaining and deadline in 2 weeks."
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Benefits of MCP

For Developers

  • Standardized: One protocol for all integrations
  • Reusable: Servers work with any MCP client
  • Secure: Built-in security and isolation
  • Flexible: Support for various data types and operations

For Users

  • Powerful: AI assistants can do more
  • Consistent: Same interface across different tools
  • Safe: Controlled access to sensitive data
  • Extensible: Easy to add new capabilities

MCP vs Other Approaches

Traditional APIs

  • MCP: Designed for AI interaction, includes metadata
  • REST APIs: General purpose, requires custom integration

Function Calling

  • MCP: Standardized protocol, persistent connections
  • Function Calling: Model-specific, stateless

Plugins

  • MCP: Cross-platform, secure isolation
  • Plugins: Platform-specific, shared memory space

Getting Started

As a User

  1. Install an MCP-compatible client (like Kiro, Claude Desktop, or Continue)
  2. Configure MCP servers for your needs
  3. Start using enhanced AI capabilities

As a Developer

  1. Learn the MCP specification - Official Docs
  2. Build servers for your data/tools - Use the MCP Python SDK
  3. Test with MCP clients - Try existing implementations
  4. Share with the community - Contribute to open source projects

Common Use Cases

Data Access

  • Database queries
  • File system operations
  • API integrations
  • Cloud storage access

Automation

  • System administration
  • Deployment scripts
  • Monitoring and alerts
  • Workflow automation

Development Tools

  • Code analysis
  • Testing automation
  • Documentation generation
  • Project management

Business Applications

  • CRM integration
  • Analytics and reporting
  • Customer support
  • Content management

Next Steps

Now that you understand MCP basics:

  1. Try it out: Use an existing MCP server
  2. Build something: Create your first MCP server
  3. Explore: Look at community examples
  4. Contribute: Share your servers with others

Key Takeaways

  • MCP connects AI assistants to external data and tools
  • It uses a client-server architecture with standardized communication
  • Tools perform actions, resources provide data
  • Security and isolation are built into the protocol
  • It's designed specifically for AI interaction patterns

Additional Resources

📖 Official Documentation

🛠️ Development Tools

🤝 Community & Examples

Ready to start building with MCP? Explore the AI & MCP Toolkit for practical examples and ready-to-use servers!

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