Building RAG Applications with MCP

Retrieval-Augmented Generation (RAG) has become one of the most powerful patterns for enhancing LLM capabilities with external knowledge. The Model Context Protocol (MCP) makes implementing RAG systems significantly easier and more flexible. In this post, we'll walk through building a RAG application using MCP-compatible servers: ## The RAG Architecture 1. **Document Processing**: First, we'll process and chunk our documents into manageable pieces. 2. **Vector Storage**: Next, we'll store these chunks in a vector database for semantic retrieval. 3. **Query Processing**: When a user query comes in, we'll retrieve the most relevant context. 4. **Augmented Generation**: Finally, we'll use an MCP server to generate a response based on the retrieved context. ## Benefits of Using MCP for RAG - **Model Flexibility**: Easily switch between different LLM providers without changing your RAG implementation. - **Standardized Context Handling**: MCP provides consistent methods for passing context to models. - **Improved Evaluation**: The standardized interface makes it easier to compare performance across different models. In the next sections, we'll provide code examples and best practices for implementing each component of our RAG system using popular MCP servers like LlamaIndex and Langchain.