Build an MCP server which answers questions with retrieval augmented generation

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

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]...

Best for

Engineering automation workflows
AI RAG automation workflows
intermediate n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.mcptrigger, @n8n/n8n-nodes-langchain.vectorstoreqdrant, @n8n/n8n-nodes-langchain.embeddingsollama, n8n-nodes-base.stickynote, n8n-nodes-base.formtrigger, @n8n/n8n-nodes-langchain.documentdefaultdataloader

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Thomas Janssen.

Original n8n.io source

1.1 Workflow description

Title: Build an MCP server which answers questions with retrieval augmented generation
Workflow name: Build an MCP server which answers questions with retrieval augmented generation

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 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

Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit
Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model.
Activate the n8n flow

Run the "RAG Ingestion Pipeline" and upload some PDF documents

How to use it

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

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - MCP Server Trigger

Type / Role: @n8n/n8n-nodes-langchain.mcpTrigger - mcpTrigger
Config choices: Version 2

Block 2 - Qdrant Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices: Version 1.3

Block 3 - Embeddings Ollama

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOllama - embeddingsOllama
Config choices: Version 1

Block 4 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 5 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 6 - On form submission

Type / Role: n8n-nodes-base.formTrigger - formTrigger
Config choices: Version 2.2

Block 7 - Qdrant Vector Store1

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices: Version 1.3

Block 8 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1.1

3. Summary Table

Workflow	Build an MCP server which answers questions with retrieval augmented generation
Complexity	intermediate
Nodes	8
Categories	Engineering, AI RAG
Author	Thomas Janssen
Published	27 Jun 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/5403/5403.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Engineering workflows AI RAG workflows

Frequently asked questions

What does Build an MCP server which answers questions with retrieval augmented generation do?

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]...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.

Thomas Janssen

3 workflows

Nodes

@n8n/n8n-nodes-langchain.mcptrigger @n8n/n8n-nodes-langchain.vectorstoreqdrant @n8n/n8n-nodes-langchain.embeddingsollama n8n-nodes-base.stickynote n8n-nodes-base.formtrigger @n8n/n8n-nodes-langchain.documentdefaultdataloader

Complexity

intermediate

Published 27 Jun 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/5403/5403.json

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Build an MCP server which answers questions with retrieval augmented generation

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Build an MCP Server which has access to a semantic database to perform Retrieval Augmented Generation (RAG)

Tutorial

How it works

AI Agent Template

Warning

Installation

How to use it

More detailed instructions

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - MCP Server Trigger

Block 2 - Qdrant Vector Store

Block 3 - Embeddings Ollama

Block 4 - Sticky Note

Block 5 - Sticky Note1

Block 6 - On form submission

Block 7 - Qdrant Vector Store1

Block 8 - Default Data Loader

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions