Build an MCP server which answers questions with retrieval augmented generation
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
Nodes used (5)
- MCP Server Trigger
@n8n/n8n-nodes-langchain.mcpTrigger - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - Embeddings Ollama
@n8n/n8n-nodes-langchain.embeddingsOllama - n8n Form Trigger
n8n-nodes-base.formTrigger - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader
What it does
Build an MCP Server which has access to a semantic database to perform Retrieval Augmented Generation (RAG) Tutorial !thumbnail.png(fileId:1878) Click here to watch the full tutorial on YouTube How it works This MCP Server has access to a local semantic database (Qdrant) and answers questions being asked to the MCP Client. AI Agent Template Click here to navigate to the AI Agent n8n workflow which uses this MCP server Warning This flow only runs local and cannot be executed on the n8n cloud platform because of the MCP Client Community Node. Installation 1. Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit 2. Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. 3. Activate the n8n flow !activate n8n flow.png(fileId:1593) 4. Run the "RAG Ingestion Pipeline" and upload some PDF documents How to use it 1. Run the MCP Client workflow and ask a question. It will be either answered by using the semantic database or the search engine API. More detailed instructions Missed a step? Find more detailed instructions here: https://brightdata.com/blog/ai/news-feed-n8n-openai-bright-data