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.
What this workflow does
This AI workflow has 6 nodes and runs when an MCP client calls one of its tools (plus 1 other trigger). Along the way it connects Qdrant Vector Store and Embeddings Ollama.
How it runs
Trigger: When an MCP client calls one of its tools (plus 1 other trigger).
- Qdrant Vector Store: stores documents in the Qdrant vector store, with embeddings: Embeddings Ollama; document loader: Default Data Loader
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
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
From the author
Build an MCP Server which has access to a semantic database to perform Retrieval Augmented Generation (RAG) Tutorial 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…