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Build your own Qdrant vector store MCP server

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Build your own Qdrant vector store MCP server preview
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Important notice

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

1. Workflow Overview

This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation. This n8n implementation exposes other cool API features from Qd...

Best for

  • Engineering automation workflows
  • AI RAG automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.toolworkflow, @n8n/n8n-nodes-langchain.mcptrigger, n8n-nodes-base.executeworkflowtrigger, n8n-nodes-base.switch, n8n-nodes-base.httprequest, n8n-nodes-base.code, n8n-nodes-base.aggregate

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title
Build your own Qdrant vector store MCP server
Workflow name
Build your own Qdrant vector store MCP server

This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation.

This n8n implementation exposes other cool API features from Qdrant such as facet search, grouped search and recommendations APIs. With this, we can build an easily customisable and maintainable Qdrant MCP server for business intelligence.

This MCP example is based off an official MCP reference implementation which can be found here - https://github.com/qdrant/mcp-server-qdrant

How it works

  • A MCP server trigger is used and connected to 5 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task.
  • We use a mix of n8n supported Qdrant nodes for simple operations such as insert documents and similarity search, and HTTP node to hit the Qdrant API directly for Facet search, group search and recommendations.
  • We use "Edit Field" and "Aggregate" nodes to return suitable responses to the MCP client.

How to use

  • This Qdrant MCP server allows any compatible MCP client to manage a Qdrant Collection by supporting select and create operations. You will need to have a collection available before you can use this server. Use the Prerequisite manual steps to get started!
  • Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  • Try the following queries in your MCP client:
    • "Can you help me list the available companies in the collection?"
    • "What do customers say about product deliveries from company X?"
    • "What do customers of company X and company Y say about product ease of use?"

Requirements

  • Qdrant for vector store. This can be an a cloud-hosted instance or one you can self-host internally.
  • MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

Customising this workflow

  • Depending on what queries you'll receive, adjust the tool inputs to make it easier for the agent to set the right parameters.
  • Not interested in Reviews? The techniques shared in this template can be used for other types of collections.
  • Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

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 - Sticky Note

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

Block 2 - Insert

Type / Role
@n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices
Version 2.1

Block 3 - Search

Type / Role
@n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices
Version 2.1

Block 4 - Recommend

Type / Role
@n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices
Version 2.1

Block 5 - Qdrant MCP Server

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

Block 6 - When Executed by Another Workflow

Type / Role
n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
Config choices
Version 1.1

Block 7 - Operation

Type / Role
n8n-nodes-base.switch - switch
Config choices
Version 3.2

Block 8 - Sticky Note1

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

Block 9 - Compare

Type / Role
@n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices
Version 2.1

Block 10 - Sticky Note8

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

Block 11 - Recommend API

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 12 - Get Embeddings

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 13 - Preferences to Items

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 14 - Aggregate Embeddings

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

Block 15 - Get Embeddings1

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 16 - Aggregate Embeddings1

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

Block 17 - Group Search API

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 18 - Has Results?

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

Block 19 - Simplify Group Results

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 20 - Empty Compare Response

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 21 - Aggregate Compare Response

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

Block 22 - Embeddings OpenAI

Type / Role
@n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices
Version 1.2

Block 23 - Default Data Loader

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

Block 24 - Recursive Character Text Splitter

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

Showing the first 24 of 44 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Build your own Qdrant vector store MCP server
Complexity advanced
Nodes 44
Categories Engineering, AI RAG
Author Jimleuk
Published 21 Apr 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/3636/3636.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

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

  4. 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. 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.

Frequently asked questions

What does Build your own Qdrant vector store MCP server do?

This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation. This n8n implementation exposes other cool API features from Qd...

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.