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Integrating MCP with AI Features

One of the primary use cases for the Model Context Protocol (MCP) is providing structured context to AI models. This guide explains how to integrate MCP with AI features in your Zopio application.

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Basic Integration

To integrate MCP with AI features, you’ll typically follow these steps:

  1. Set up an MCP server to host your context resources
  2. Create an MCP client to fetch those resources
  3. Use the resources as context in AI requests

Here’s a basic example:

import { MCPClient } from '@repo/mcp';
import { generateText } from '@repo/ai';
import { models } from '@repo/ai/lib/models';

// Create MCP client
const mcpClient = new MCPClient({
  serverUrl: '/api/mcp'
});

// Fetch user context
const userContext = await mcpClient.readResource('user', userId);

// Use context in AI request
const aiResponse = await generateText({
  model: models.chat,
  prompt: 'Generate a personalized greeting',
  context: {
    user: userContext
  }
});

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Advanced Context Building

For more complex scenarios, you can build a comprehensive context object from multiple resources:

import { MCPClient } from '@repo/mcp';
import { generateText } from '@repo/ai';
import { models } from '@repo/ai/lib/models';

async function buildAIContext(userId: string) {
  const mcpClient = new MCPClient({
    serverUrl: '/api/mcp'
  });

  // Fetch multiple resources in parallel
  const [user, preferences, recentActivity] = await Promise.all([
    mcpClient.readResource('user', userId),
    mcpClient.readResource('preferences', userId),
    mcpClient.readResource('activity', userId)
  ]);

  // Build a comprehensive context object
  return {
    user,
    preferences,
    recentActivity,
    timestamp: new Date().toISOString()
  };
}

// Use the context builder in an AI request
const context = await buildAIContext(userId);
const aiResponse = await generateText({
  model: models.chat,
  prompt: 'Generate personalized content recommendations',
  context
});

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Streaming Responses with Context

When using streaming responses, you can still provide MCP context:

import { MCPClient } from '@repo/mcp';
import { streamText } from '@repo/ai';
import { models } from '@repo/ai/lib/models';

// Create MCP client
const mcpClient = new MCPClient({
  serverUrl: '/api/mcp'
});

// Fetch product context
const productContext = await mcpClient.readResource('product', productId);

// Stream AI response with context
const stream = await streamText({
  model: models.chat,
  prompt: 'Generate a detailed product description',
  context: {
    product: productContext
  }
});

// Process the stream
for await (const chunk of stream) {
  console.log(chunk);
}

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Using MCP with AI Tools

MCP resources can be used to provide context to AI tools and agents:

import { MCPClient } from '@repo/mcp';
import { generateText } from '@repo/ai';
import { models } from '@repo/ai/lib/models';
import { productToolkit } from '@repo/products/ai';

// Create MCP client
const mcpClient = new MCPClient({
  serverUrl: '/api/mcp'
});

// Fetch catalog context
const catalogContext = await mcpClient.readResource('catalog', catalogId);

// Use context with AI tools
const response = await generateText({
  model: models.chat,
  tools: {
    ...productToolkit.getTools(),
  },
  context: {
    catalog: catalogContext
  },
  maxSteps: 5,
  prompt: 'Find products in the catalog that match these criteria: price under $100, category: electronics'
});

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Best Practices

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1. Keep Context Focused

Only include relevant information in your context. Large context objects can:

  • Increase token usage
  • Slow down processing
  • Potentially confuse the AI model

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2. Structure Context Hierarchically

Organize your context in a logical hierarchy:

const context = {
  user: userContext,
  session: {
    currentPage: 'product-listing',
    filters: {
      category: 'electronics',
      priceRange: { min: 0, max: 1000 }
    }
  },
  application: {
    version: '1.2.3',
    features: ['recommendations', 'reviews']
  }
};

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3. Include Metadata

Add metadata to help the AI model understand the context:

const context = {
  user: userContext,
  _metadata: {
    timestamp: new Date().toISOString(),
    version: '1.0',
    source: 'user-service'
  }
};

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4. Validate Context Before Use

Always validate your context before sending it to AI models:

import { validateResource } from '@repo/mcp';
import { userSchema } from '@repo/mcp';

// Validate user context
if (!validateResource(userContext, userSchema)) {
  throw new Error('Invalid user context');
}

​
5. Handle Missing Context Gracefully

Implement fallbacks for when context resources are not available:

let userContext;
try {
  userContext = await mcpClient.readResource('user', userId);
} catch (error) {
  console.warn('Could not fetch user context:', error);
  userContext = {
    id: userId,
    type: 'user',
    attributes: {
      name: 'Anonymous User'
    }
  };
}

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Example: Personalized AI Assistant

Here’s a complete example of using MCP to build a personalized AI assistant:

import { MCPClient } from '@repo/mcp';
import { generateText } from '@repo/ai';
import { models } from '@repo/ai/lib/models';

export async function getPersonalizedAssistant(userId: string) {
  const mcpClient = new MCPClient({
    serverUrl: '/api/mcp'
  });

  // Build comprehensive user context
  const [user, preferences, history] = await Promise.all([
    mcpClient.readResource('user', userId),
    mcpClient.readResource('preferences', userId),
    mcpClient.readResource('conversation-history', userId)
  ]);

  // Create a system prompt using the context
  const systemPrompt = `
    You are a personal assistant for ${user.attributes.name}.
    Preferred language: ${preferences.attributes.language || 'English'}
    Communication style: ${preferences.attributes.communicationStyle || 'Friendly'}
    Areas of interest: ${preferences.attributes.interests?.join(', ') || 'Not specified'}
    
    Please provide personalized assistance based on this information.
  `;

  // Return a function that generates responses
  return async (userMessage: string) => {
    return generateText({
      model: models.chat,
      systemPrompt,
      prompt: userMessage,
      context: {
        user,
        preferences,
        recentConversations: history.attributes.recent || []
      }
    });
  };
}

// Usage
const assistant = await getPersonalizedAssistant('user-123');
const response = await assistant('What should I do this weekend?');