Haystack (deepset Cloud) MCP. Audit, test, and run your RAG pipelines from natural conversation.
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Haystack (deepset Cloud) MCP lets your AI agent manage complex Retrieval Augmented Generation (RAG) pipelines and search massive document sets.
You can list isolated workspaces, run full-scale NLP topologies, trigger immediate vector searches, and inspect metadata attached to source documents—all through natural conversation.
What your AI agents can do
Get file
Retrieves specific metadata attached to an uploaded source file.
Get pipeline
Fetches detailed information about a single, existing AI pipeline topology.
List files
Provides a list of all files that have been uploaded to the knowledge base.
You can list available workspaces, keeping different search contexts and projects separate.
The tool lets you view the details of existing AI pipelines or get metadata about source files.
You can dispatch an immediate pipeline run to test retrieval logic and see what results come back from your indexed knowledge.
The agent triggers dense or sparse vector searches directly over all the documents you’ve uploaded into the index.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Haystack Deepset Cloud: 7 Tools
Use these seven tools to list workspaces, inspect pipeline topologies, search documents, and manage the full lifecycle of your RAG data.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Haystack (deepset Cloud) on Vinkius019d75aeget file
Retrieves specific metadata attached to an uploaded source file.
019d75aeget pipeline
Fetches detailed information about a single, existing AI pipeline topology.
019d75aelist files
Provides a list of all files that have been uploaded to the knowledge base.
019d75aelist pipelines
Generates a comprehensive list of all active AI pipelines available in your account.
019d75aelist workspaces
Lists the separate, isolated environments used for different search contexts.
019d75aerun pipeline
Executes a full AI pipeline search using specific parameters to test retrieval logic.
019d75aesearch documents
Triggers a dense or sparse vector search across all indexed enterprise documents.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Today, checking your AI context is an audit nightmare.
Right now, if you want to know why your agent gave a certain answer, you have to jump through five different internal dashboards. You check the workspace setting, then cross-reference the document ID in a separate metadata panel, and finally run a manual test search just to see which pipeline was active. It’s slow, it's multi-tab clicking, and it takes half an hour just to verify the source.
With this MCP, you tell your agent what you need—like listing all workspaces or running a specific vector search—and the system handles all those clicks and cross-references for you. You get immediate confirmation of context integrity without leaving your chat window.
The Haystack (deepset Cloud) MCP gives you full RAG visibility.
You don't have to manually list pipelines, check their configuration (`get_pipeline`), or worry if the file is even indexed. The agent handles these checks for you; it just knows that 'this workspace has a working pipeline ready for search.'
It’s not just about getting an answer. It's about knowing *how* the answer was generated, which is something this MCP lets your agent do effortlessly.
What you can do with this MCP connector
This connector gives you deep access to running RAG pipelines using your deepset Cloud account. Instead of building complex API calls every time you need context, you talk to your agent about it. You can list isolated environments (workspaces) for different projects and then inspect the full NLP topologies, seeing exactly where embedding nodes or retriever logic are placed.
Need to test a pipeline? Just ask your agent to run a search using specific pipelines, dispatching immediate LLM or Retriever invocations against your data. It’s all about making sure your AI answers come from verifiable sources. By connecting this MCP through Vinkius, you get to manage the entire flow—from listing files and checking metadata to triggering dense vector searches across enterprise knowledge bases.
This means your agent doesn't just guess; it grounds every answer in documented truth.
019d75ae-98e9-73b9-b860-05c68d5f1e3b 
How Haystack (deepset Cloud) MCP Works
- 1 Subscribe to this MCP and provide your deepset Cloud API URL and key.
- 2 Your AI client uses these credentials to establish a connection to your cloud account.
- 3 You interact with the agent naturally, asking it to list workspaces or run specific searches against your connected knowledge base.
The bottom line is, you use natural conversation to execute complex ML tasks that previously required multiple API calls and environment setup.
Who Is Haystack (deepset Cloud) MCP For?
This MCP is for the AI/ML engineer who needs to audit pipeline configurations fast. It's also for data scientists building enterprise RAG systems, or product managers verifying that document indexing works exactly how it should.
Using the MCP, they can list pipelines and run searches to quickly validate if their new embedding nodes are pulling the right context from various source files.
They use this connector to integrate internal knowledge bases with their agents, ensuring that all generated answers cite specific, verifiable documents found via vector search.
A PM can monitor the search performance by listing files and inspecting metadata to verify that document indexing status is correct before a major rollout.
What Changes When You Connect
- Test retrieval logic immediately. Use the
run_pipelinetool to dispatch immediate LLM or Retriever invocations, confirming that your agent pulls accurate context before production deployment. - Manage scope with isolated environments. The ability to use
list_workspacesmeans you can test different projects without worrying about cross-contamination of search data. - Audit the full pipeline structure. Use
get_pipelineandlist_pipelinesto visualize NLP topologies, letting you see exactly how embedding nodes and retriever logic are wired up. - Verify document sources. Before building an agent that cites facts, use
search_documentsto trigger vector searches over your index and confirm the relevance of retrieved data snippets. - Inspect file context easily. You can run
list_filesthen useget_fileto inspect metadata on individual source documents, proving where the information came from.
Real-World Use Cases
Debugging a poor answer
The team noticed an agent was giving outdated compliance advice. They used list_workspaces to find the correct 'Compliance' environment, then triggered a focused search using run_pipeline. The results pointed directly to an incorrectly indexed file, allowing them to fix the source data.
Onboarding new knowledge
A product team added 50 new technical manuals. Instead of manually checking every document, they used list_files and then ran search_documents with a query to ensure the embeddings successfully captured key terms from all the new material.
Comparing two models
A data scientist wanted to compare how two different retrieval mechanisms performed. They used list_pipelines to find both 'Model A' and 'Model B', then ran separate searches using run_pipeline on the same query, allowing a direct comparison of snippet quality.
Verifying data integrity
The PM needed confirmation that certain sensitive metadata was attached to key documents. They used list_files to find the target document path and then called get_file to inspect the exact metadata fields, proving compliance.
The Tradeoffs
Treating it like a basic search engine
Just calling an agent and asking 'What's the answer?' without specifying the source context or testing the pipeline first. You get vague, ungrounded answers.
→
Always confirm your setup first. Use list_workspaces to select the right environment, then use run_pipeline to test the full retrieval path before relying on a conversational answer.
Manually checking file paths
Trying to recall or manually input every document name or ID when debugging. This is slow and error-prone.
→
Start by calling list_files to get a full directory listing, then use the resulting names with get_file for quick metadata checks.
Assuming all pipelines are ready
Running a critical search without checking if the pipeline exists or is configured correctly. The agent will fail mid-process.
→
First, use list_pipelines to confirm which topologies are available, then target that specific name using run_pipeline.
When It Fits, When It Doesn't
Use this MCP if your core problem is grounding an AI agent's response in complex, structured enterprise data. You need more than just simple keyword search; you need to audit the entire RAG process—from workspace selection and file indexing (list_files) to running full NLP topologies (run_pipeline). Don't use this if you just need a single API call to fetch one piece of unstructured text. For that, a simple data retrieval tool is better. This MCP shines when you are in the developer or ML role, validating the mechanisms behind the search, not just getting the final answer.
Common Questions About Haystack (deepset Cloud) MCP
How do I start testing my RAG pipelines with search_documents? +
You trigger a vector search by asking your agent to execute search_documents. This runs dense or sparse searches over all indexed documents, giving you the raw results needed for testing.
What is the difference between list_pipelines and run_pipeline? +
list_pipelines just shows you names; it doesn't do anything. run_pipeline, however, executes a full search using one of those listed topologies to test its real-world output.
Can I check the metadata for a single document using get_file? +
Yes. You can use get_file and provide the file's path or ID to retrieve specific metadata attached to that source document embedding.
Does this MCP help me manage different project contexts? +
Absolutely. You use list_workspaces to see all isolated environments, which ensures your agent only searches the documents relevant to the current project or context you're working on.
How do I use list_files to verify which documents are available before running a search? +
The MCP lets you run list_files first. This shows you all the uploaded files in a workspace, letting your agent know exactly what data it can reference before attempting a complex query or building a pipeline.
If my RAG results are inaccurate, how can I use get_pipeline to debug the underlying logic? +
You run get_pipeline to inspect the NLP topology. This lets you verify if the embedding nodes and retriever logic are configured correctly for your specific knowledge base, which is key for accurate results.
When using list_workspaces, how does the MCP ensure my agent queries an isolated environment? +
The system separates contexts by workspace ID. Your AI client uses this ID to guarantee that when you execute a search or run a pipeline, it only accesses data from the specified, isolated knowledge base.
What should I do if my attempt to run_pipeline fails? +
The MCP will return specific API error details. You check those logs to see if the failure is due to outdated credentials or if the pipeline needs manual adjustment of its source documents.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.