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RAG MCP Server (Lambda + OpenSearch Serverless)

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Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

0x00000002

About RAG MCP Server

No overview available yet

RAG MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "rag-mcp-server": {
      "command": "python",
      "args": [
        "example.py"
      ]
    }
  }
}

RAG MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is RAG MCP Server (Lambda + OpenSearch Serverless)?

It is an MCP (Model Context Protocol) server implementing a RAG (Retrieval-Augmented Generation) system using a serverless AWS architecture. The server integrates AWS Lambda, API Gateway, OpenSearch Serverless, OpenAI, and S3, and is intended for developers building AI agents that need a document retrieval and generation backend.

How to use RAG MCP Server (Lambda + OpenSearch Serverless)?

Deploy the server to your AWS account using the provided Makefile. The typical workflow is: install dependencies (make deps), bootstrap CDK (make bootstrap), create required secrets in AWS Secrets Manager (an OpenAI API key and an application API key), then deploy (make deploy). After deployment, interact with the API using the endpoint URL and the application API key in the X-API-Key header; the server exposes a /mcp endpoint for MCP discovery and execution.

Key features of RAG MCP Server (Lambda + OpenSearch Serverless)

  • Serverless RAG server on AWS
  • MCP (Model Context Protocol) compatible
  • Vector search via OpenSearch Serverless
  • Embeddings and generation with OpenAI
  • Persistent document storage on S3
  • Infrastructure defined with AWS CDK

Use cases of RAG MCP Server (Lambda + OpenSearch Serverless)

  • Add documents to a knowledge base for retrieval
  • Query the knowledge base with RAG retrieval and generation
  • List all stored documents via the API
  • Integrate with AI agents using the MCP protocol
  • Deploy a production

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