MCP Protocol in LLM Applications

What is MCP?

The Model Context Protocol (MCP) is an emerging standard for managing context in Large Language Model applications. It provides a structured way to handle conversation history, external knowledge, and tool interactions.

Why MCP Matters for RAG

Retrieval-Augmented Generation (RAG) applications face a fundamental challenge: how do you efficiently combine retrieved documents with conversation context while staying within token limits?

MCP solves this with:

• Context Windows: Structured management of what the model "sees"
• Priority Queues: Important context stays, less relevant context is pruned
• Streaming Updates: Real-time context modification during generation

Implementation with Vector Databases

Here's how to integrate MCP with a vector database like Pinecone:

import { MCPClient } from '@mcp/core';
import { PineconeClient } from '@pinecone-database/pinecone';
const mcp = new MCPClient({
  maxTokens: 8192,
  strategy: 'sliding-window'
});
async function queryWithContext(query: string) {
  const embeddings = await generateEmbedding(query);
  const results = await pinecone.query({
    vector: embeddings,
    topK: 5
  });
  mcp.addContext({
    type: 'retrieved',
    priority: 'high',
    content: results.matches.map(m => m.metadata.text)
  });
  return mcp.generate(query);
}

Best Practices

Conclusion

MCP provides the structure needed to build production-grade RAG applications. As LLMs become more capable, efficient context management becomes the differentiator between good and great AI products.