deepset Cloud Connector for AI agents.
7 live capabilities
Manage your RAG pipelines and enterprise search index through a chat interface.
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Why people use deepset Cloud
Haystack (deepset Cloud) for faster RAG pipeline auditing
This Connector puts that entire workflow into your chat interface. You can ask your agent to show you the pipelines in a workspace or run a search on the fly. You get immediate confirmation of your search logic without the manual clicking.
What Vinkius changes
You can manage your entire RAG infrastructure through a chat window instead of a complex dashboard.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Verifying document indexing
A developer needs to check if a new PDF was indexed.
- Real-world use case 02
Testing retriever logic
An ML engineer wants to see if a hybrid retriever is working.
- Real-world use case 03
Audit production pipelines
A product manager wants to see all available search options.
Complete set · 7capabilities
The complete deepset Cloud capability set.
These are the exact actions your AI can choose when you ask it to work with deepset Cloud.
01—04
4 capabilities in this set.
Part of 7 available through deepset Cloud.
- 01 Capability
List workspaces
List all workspaces. Use this to keep different projects' search contexts separate.
- 02 Capability
List pipelines
List pipelines. It helps you see what's available to run in your cloud account.
- 03 Capability
Run pipeline
Run a pipeline search. This lets you test RAG logic instantly with a natural language query.
- 04 Capability
Get pipeline
Get pipeline details. Use this to check out the exact configuration of a retriever.
05—07
3 capabilities in this set.
Part of 7 available through deepset Cloud.
- 05 Capability
List files
List uploaded files. It's the easiest way to check if your documents actually made it to the cloud.
- 06 Capability
Get file
Get file metadata. Use this to see the details attached to your source document embeddings.
- 07 Capability
Search documents
Search documents in index. This triggers the actual vector search over your enterprise knowledge.
Set up in minutes
One URL. Then ask deepset Cloud to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use deepset Cloud 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_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable deepset Cloud for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud URL.
- Step 03
Save and start
Save the connection and enable deepset Cloud in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"haystack-deepset-cloud": {
"url": "https://edge.vinkius.com/vk_preview_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud
Open Agent mode in chat and ask: "Using deepset Cloud, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"haystack-deepset-cloud": {
"url": "https://edge.vinkius.com/vk_preview_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud
Ask Copilot: "Using deepset Cloud, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"haystack-deepset-cloud": {
"url": "https://edge.vinkius.com/vk_preview_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud
Open Cascade and ask: "Using deepset Cloud, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"haystack-deepset-cloud": {
"url": "https://edge.vinkius.com/vk_preview_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud
Ask Cline: "Using deepset Cloud, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add haystack-deepset-cloud --transport http "https://edge.vinkius.com/vk_preview_Tf2g8zLZroyDlliFU39whBZWkEDykSKQVz38t3zr/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 deepset Cloud
Ask Claude: "Using deepset Cloud, show me...". 7 tools are ready
Where the request belongs
Work deepset Cloud can move forward.
This is for the RAG engineer who's tired of manually checking logs and the product manager who needs to know if the search results are actually accurate.
RAG Developer
Debugging pipeline outputs and checking retriever logic in real-time during development.
ML Engineer
Auditing embedding nodes and testing vector search performance across different models.
Product Manager
Verifying that new documents are indexed correctly and checking search quality for stakeholders.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsR2R
Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.
Cognita (RAG Framework)
Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.
LlamaIndex (AI Data Framework & RAG)
Query and manage RAG pipelines via LlamaIndex. execute natural language searches, audit indexed files, and monitor data pipelines.
LlamaCloud (Managed RAG & Parsing)
Manage RAG pipelines and document parsing via LlamaCloud. orchestrate LlamaParse jobs and audit data ingestion.
VectorShift (AI Workflow & RAG Automation)
Automate AI workflows and RAG via VectorShift. manage pipelines, query knowledge bases, and deploy chatbots directly from any AI agent.
FastGPT
Manage FastGPT Knowledge Bases. automate dataset creation, document ingestion, and RAG search directly from any AI agent.
Bring your own AI
Change the model, client or framework. Keep deepset Cloud connected.
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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 deepset Cloud.
The practical details behind the request, access and result.
What can I do with the Haystack (deepset Cloud) MCP?
You can manage your deepset Cloud RAG pipelines, view your workspaces, and query your indexed documents directly through your AI agent.
Can I run actual searches on my indexed data using the Haystack (deepset Cloud) MCP?
Yes. You can ask your agent to run a search on a specific pipeline, and it will return the relevant snippets and source documents from your index.
How does the Haystack (deepset Cloud) MCP help with RAG development?
It simplifies the 'ops' side of RAG by letting you audit pipelines, check file statuses, and test search results in a conversational interface.
Can I see which documents are actually uploaded with the Haystack (deepset Cloud) MCP?
Yes, you can ask your agent to list the files in any workspace to verify that your source documents have been successfully indexed.
How do I manage different search environments using the Haystack (deepset Cloud) MCP?
You can use the workspace capabilities to list and switch between different isolated environments for different projects or search contexts.
Is the Haystack (deepset Cloud) MCP good for testing NLP topologies?
It's great for that. You can quickly pull the details of any pipeline to see how your embedding nodes and retrievers are configured.
Can I test my RAG pipelines directly via my AI agent?
Yes. Use the run_pipeline capability to dispatch a query to any registered pipeline in your workspace. Your agent will return the response from the NLP topology, allowing you to verify retriever performance and LLM grounding without leaving your workspace.
How can I audit my document indexing status?
Ask your agent to list files in your workspace. You can then get specific metadata for any file to ensure embeddings and attributes are correctly attached. This is essential for debugging retrieval issues in production environments.
Is it possible to manage multiple deepset Cloud workspaces?
Absolutely. The agent provides high-level workspace listing, allowing you to navigate across tenant boundaries and isolation zones easily. You just need to provide the workspace name to any pipeline or search command.
One connection away
Give your agent a direct line to deepset Cloud.
Connect deepset Cloud once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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