Qdrant Connector for AI agents.
7 live capabilities
Query vector embeddings and manage collections in your RAG pipeline.
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Why people use Qdrant
Qdrant Vector Database Management
This Connector lets your AI agent do that work for you. You can just ask it to show you the payload for a specific ID or list every collection in your cluster. It turns a multi-step manual audit into a single conversation.
What Vinkius changes
You get direct, conversational access to your vector database's internal data and operations.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
RAG Pipeline Debugging
An engineer asks the agent to scroll through the 'docs' collection to see if the metadata is correctly attached to the vectors.
- Real-world use case 02
Data Cleanup
A developer tells the agent to delete all points in a 'test_batch' collection to clear space for a new run.
- Real-world use case 03
Cluster Audit
A data scientist asks the agent to list all collections to check which ones are using Cosine versus Dot product.
Complete set · 7capabilities
The complete Qdrant capability set.
These are the exact actions your AI can choose when you ask it to work with Qdrant.
01—04
4 capabilities in this set.
Part of 7 available through Qdrant.
- 01 Capability
Get collection
Retrieves detailed information about a specific collection. Use it to check distance metrics and point counts.
- 02 Capability
Search
Performs a nearest neighbor vector search using a JSON array of floats. It lets your agent find the most relevant data points.
- 03 Capability
Get points
Retrieves specific points by their unique IDs. This is useful for checking if a particular record was saved correctly.
- 04 Capability
Scroll
Returns points with their payloads for pagination. Use it to browse through large datasets without loading everything at once.
05—07
3 capabilities in this set.
Part of 7 available through Qdrant.
- 05 Capability
Count
Counts the total number of points in a specific collection. It gives you a quick way to verify your indexing numbers.
- 06 Capability
List collections
Lists all collections in your Qdrant instance. It helps you see every vector set in your cluster at once.
- 07 Capability
Delete
Deletes specific points from a collection permanently. Use it to remove old or incorrect data from your vector space.
Set up in minutes
One URL. Then ask Qdrant to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Qdrant from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Qdrant, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Qdrant for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Qdrant URL.
- Step 03
Save and start
Save the connection and enable Qdrant in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"qdrant": {
"url": "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Qdrant
Open Agent mode in chat and ask: "Using Qdrant, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"qdrant": {
"url": "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Qdrant
Ask Copilot: "Using Qdrant, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"qdrant": {
"url": "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Qdrant
Open Cascade and ask: "Using Qdrant, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"qdrant": {
"url": "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Qdrant
Ask Cline: "Using Qdrant, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add qdrant --transport http "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Qdrant
Ask Claude: "Using Qdrant, show me...". 7 tools are ready
Where the request belongs
Work Qdrant can move forward.
This is for the engineer who's tired of context-switching between a database console and their IDE. It's for anyone building RAG systems who needs to see what's actually happening inside their vector space.
AI & ML Engineer
Debugging RAG pipelines by inspecting embeddings and verifying similarity search results in real time.
Data Scientist
Testing distance parameters on live indices without needing to launch a Jupyter Notebook.
Backend Developer
Managing vector cluster configurations and clearing out bad datasets efficiently from the console.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsMilvus (Open-Source Vector Database)
Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.
Zilliz Cloud
Manage vector collections and perform similarity searches via Zilliz Cloud.
Chroma (Vector DB)
Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly from any AI agent.
OpenSearch Vector
Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.
Weaviate
Search and manage vector data on Weaviate. the AI-native database for building production-grade AI applications.
Typesense Vector Search
Automate vector similarity searches via Typesense. index documents, manage collections, and execute semantic queries directly from your AI agent.
Bring your own AI
Change the model, client or framework. Keep Qdrant connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Qdrant.
The practical details behind the request, access and result.
Can I use Qdrant MCP to manage my vector data?
Yes, you can list, count, and delete points directly. This allows you to manage your database content through a natural conversation with your AI agent.
How do I use Qdrant MCP for RAG debugging?
You can use it to scroll through payloads and check embedding quality. It helps you verify that your metadata is correctly attached to your vectors during development.
Does Qdrant MCP support similarity searches?
Yes, it can perform nearest neighbor searches using float arrays. This makes it easy to test how your agent retrieves information from your vector space.
Can Qdrant MCP help me clean up my vector database?
You can use the delete capability to remove specific points. This is great for clearing out test data or removing incorrect entries without manual scripts.
Is Qdrant MCP safe for production clusters?
It is a capability for interaction and auditing. While it allows for deletions, you should always be intentional with those commands in a production environment.
How do I connect Qdrant MCP to Claude?
You can connect it by adding your Qdrant Base URL and API Key in the Vinkius setup. Once connected, Claude can query your embeddings directly.
How do I find my Qdrant URL and API Key?
For Qdrant Cloud: Go to the Qdrant Cloud Console, select your cluster to open the Cluster Detail Page. The endpoint will be displayed there (e.g., xyz.us-east4-0.gcp.cloud.qdrant.io), and you can generate Database API Keys underneath it (they start with eyJhb). For Self-hosted: Provide your custom URL and the static custom key you defined in your config.yaml.
Can my AI use this for a RAG architecture directly?
Yes contextually, but practically your agent acts as the database debugger. It can formulate vector arrays to query search_points, retrieving identical payload structures. It's meant for the engineer building the RAG, helping you inspect distances and debug faulty retrieval mechanisms mid-code.
Does it support deleting vectors?
Yes. If an embedding got corrupted or references dropped articles, use the delete capability. Pass the collection name and the list of specific IDs. Qdrant handles the mutation instantly and updates the index without rebuilding.
What if I have millions of points?
Instead of overloading your chat context, instruct your agent to use the count capability to grasp the scale, and the scroll capability with a small limit constraint (e.g., 5-10 records at a time). This paginates large bodies cleanly when analyzing index health.
One connection away
Give your agent a direct line to Qdrant.
Connect Qdrant once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available