MCP로 RAG Application 구현하기

What is MCP로 RAG Application 구현하기?

MCP로 RAG Application 구현하기 is a guide and reference implementation for building a Retrieval-Augmented Generation (RAG) application using the Model Context Protocol (MCP). It runs on AWS with Streamlit for the UI, uses LangGraph for agent orchestration, Amazon Knowledge Base for managed document processing, and AWS Lambda for serverless RAG retrieval. It is intended for developers who want to integrate external data sources into generative AI applications via MCP.

How to use MCP로 RAG Application 구현하기?

Install the required packages: pip install mcp langchain-mcp-adapters. Set up an MCP server by defining a Python script (e.g., application/mcp-server.py) that uses FastMCP and exposes a search tool. Configure the server in a config.json file (format: {"mcpServers": {"search": {"command": "python", "args": ["application/mcp-server.py"]}}}). Run the Streamlit application, which uses asyncio.run() to asynchronously invoke the MCP agent. For multiple servers, use MultiServerMCPClient from langchain_mcp_adapters.

Key features of MCP로 RAG Application 구현하기

• Implements MCP (Model Context Protocol) for external data access
• Integrates LangChain MCP Adapters to connect tools to LangGraph agents
• Uses Amazon Knowledge Base for managed document chunking, sync, and multimodal support
• Deploys AWS Lambda for serverless RAG retrieval with optional grading and multi-region parallel processing
• Provides a Streamlit UI served via ALB and CloudFront over HTTPS
• Supports both single and multiple MCP servers via MultiServerMCPClient

Use cases of MCP로 RAG Application 구현하기

• Querying a knowledge base of documentation through a conversational AI assistant
• Performing RAG-based search over enterprise documents with custom grading
• Aggregating results from multiple MCP servers (e.g., Google Search, custom RAG)
• Building a customizable agent that uses MCP tools for real-time data retrieval

FAQ from MCP로 RAG Application 구현하기

What is MCP (Model Context Protocol)?

MCP is an open-source protocol introduced by Anthropic in November 2024. It provides a standardized interface for generative AI applications to access external data sources. MCP servers expose capabilities (tools, resources, prompts) via JSON-RPC 2.0 over stdio or SSE.

How does this application differ from a plain RAG system?

This application uses MCP to decouple the retrieval logic from the agent. The RAG function is encapsulated in an MCP server, which can be swapped or combined with other MCP servers. The agent (built with LangGraph) communicates with the MCP client, which in turn talks to the server, allowing flexibility in tool composition.

What are the transport options for MCP communication?

MCP supports two transport modes: stdio (standard input/output, suitable for local execution) and SSE (Server-Sent Events, for network communication). The README demonstrates stdio for the RAG server, while the overall architecture uses SSE between MCP client and server when running on separate hosts.

Do I need an AWS account to run this application?

Yes. The implementation relies on AWS services: Amazon Knowledge Base (for RAG), AWS Lambda (for query processing), and optionally multi-region parallel execution. The README mentions deploying infrastructure using AWS CDK and configuring AWS credentials via aws configure.

Can I use this application with multiple MCP servers?

Yes. The README shows how to use MultiServerMCPClient from langchain_mcp_adapters to connect to several MCP servers simultaneously. Each server’s tools are aggregated and made available to the LangGraph agent.