What is the Model Context Protocol?

MCP Overview

This guide introduces the Model Context Protocol (MCP) and its core concepts:

• Protocol basics - Understand what MCP is and how it standardizes AI interactions
• Core features - Explore context management, tools, and resources in MCP
• Key benefits - Learn why MCP is better than direct API integration
• Implementation flow - See how MCP works in a typical application

The Model Context Protocol (MCP) is an open standard that defines how applications can interact with AI models while maintaining context across conversations. It simplifies AI application development by providing a standardized approach to context management, tool integration, and resource handling.

Key Concepts

Context Management

MCP automatically manages conversation context, allowing models to remember previous interactions without requiring manual context handling.

// Creating a conversation with automatic context management
const conversation = client.createConversation({
  contextId: 'user-123',
});

// First message
const response1 = await conversation.sendMessage("Hello");

// Second message - context is automatically maintained
const response2 = await conversation.sendMessage("What did I just say?");

Tools

Tools are functions that models can call to perform actions or retrieve information. They follow a standardized format that makes them easily discoverable and usable by AI models.

// Example calculator tool
const calculatorTool = {
  name: 'calculator',
  description: 'Performs arithmetic operations',
  parameters: {
    type: 'object',
    properties: {
      operation: {
        type: 'string',
        enum: ['add', 'subtract', 'multiply', 'divide'],
        description: 'The operation to perform',
      },
      // ... other parameters
    },
  },
  execute: async (params) => {
    // Implementation
  },
};

Resources

Resources provide models with access to external data sources such as databases, knowledge bases, or APIs. They allow models to retrieve relevant information during a conversation.

// Example document resource
const documentsResource = {
  name: 'documents',
  description: 'Access to company documentation',
  fetch: async (query) => {
    // Retrieve documents based on query
    return [
      { id: '1', title: 'Getting Started', content: '...' },
      // ... other results
    ];
  },
};

Why Use MCP?

Key benefits

This section highlights the fundamental advantages of using MCP in your AI applications:

• Simplified development - Reduce complexity and development time with automatic context management
• Vendor independence - Avoid lock-in by easily switching between AI model providers
• Enhanced capabilities - Enable AI models to take actions and access data through standardized interfaces
• Optimized performance - Reduce costs and improve response times with efficient context handling

Simplified Context Management

MCP handles conversation context automatically, eliminating the need for complex state management code.

// Without MCP: Manual context handling
const messages = [];
messages.push({ role: "user", content: "Hello" });
const response1 = await model.generate({ messages });

// Save new context
messages.push({ role: "assistant", content: response1 });
messages.push({ role: "user", content: "What did I just say?" });
const response2 = await model.generate({ messages });

// With MCP: Automatic context handling
const conversation = client.createConversation({ contextId: 'user-123' });
const response1 = await conversation.sendMessage("Hello");
const response2 = await conversation.sendMessage("What did I just say?");

Standardized Integration

Connect to any MCP-compatible AI model with a consistent API, reducing vendor lock-in.

// Configure once, switch providers anytime
const server = new MCPServer({
  modelProvider: 'openai', // Easily change to 'anthropic', 'cohere', etc.
  modelConfig: {
    apiKey: process.env.API_KEY,
    model: 'gpt-4'
  }
});

Tool Integration

Enable AI models to perform actions through a standardized tool interface.

// Register tools once, works with any provider
server.registerTool({
  name: 'weather',
  description: 'Get weather information for a location',
  parameters: {
    location: { type: 'string', description: 'City name or coordinates' }
  },
  execute: async ({ location }) => {
    // Implementation
    return { temperature: 72, conditions: 'sunny' };
  }
});

Resource Access

Give models access to external data through a unified resource system.

// Provide consistent data access
server.registerResource({
  name: 'company_knowledge',
  description: 'Access to company documentation',
  fetch: async (query) => {
    // Implementation to retrieve relevant data
    return [{ title: 'Getting Started', content: '...' }];
  }
});

Cost Efficiency

Optimized context handling reduces token usage and lowers API costs.

// MCP automatically optimizes context size
const conversation = client.createConversation({
  contextId: 'user-123',
  contextOptions: {
    maxTokens: 2000,         // Limit context size
    summarizationThreshold: 0.8  // Auto-summarize when needed
  }
});

Enhanced Security

Built-in security features to protect sensitive information.

// Secure by default
const server = new MCPServer({
  security: {
    contentFiltering: true,  // Filter sensitive information
    authentication: true,    // Require authentication
    accessControl: {
      tools: ['allowed_tool_1', 'allowed_tool_2'],
      resources: ['allowed_resource_1']
    }
  }
});

How MCP Works

MCP Communication Flow

The Model Context Protocol defines a standard communication flow between applications, MCP servers, and AI models:

1. Client Request - An application sends a message or action request to an MCP server
2. Context Retrieval - The MCP server retrieves the relevant conversation context
3. AI Model Integration - The request and context are sent to the AI model
4. Tool Execution - If the model requests to use tools, the MCP server executes them
5. Response Generation - The model generates a response based on the context and tool results
6. Context Update - The MCP server updates the conversation context with the new interaction

MCP vs. Direct API Calls

Feature	MCP	Direct API Calls
Context Management	Automatic	Manual
Tool Integration	Standardized	Varies by provider
Vendor Switching	Simple	Complex
Deployment	Serverless or self-hosted	Varies
Security	Built-in features	Custom implementation
Cost Optimization	Automatic	Manual

Getting Started with MCP

Quick Start Guide

Ready to build your first MCP application? Follow these steps:

1. Installation - Set up the TRMX AI SDK and CLI
2. Quick Start - Build your first MCP application
3. Learn Architecture - Understand MCP architecture
4. Create an MCP Server - Build a custom MCP server

Next Steps

Continue Learning

• Context Management - Learn how MCP manages conversation context
• MCP Architecture - Understand the technical architecture of MCP
• Security - Explore MCP security features