Can I test my RAG pipelines directly via my AI agent?

Yes. Use the `run_pipeline` tool 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.

Haystack (deepset Cloud) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes

Name: Haystack (deepset Cloud)
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Haystack (deepset Cloud) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Haystack (deepset Cloud). "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Haystack (deepset Cloud)?"
    )
    print(response)

asyncio.run(main())

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About Haystack (deepset Cloud) MCP Server

Connect your deepset Cloud account to any AI agent and manage your Haystack-powered RAG pipelines through natural conversation.

LlamaIndex agents combine Haystack (deepset Cloud) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Workspaces — List and manage isolated environments for different search contexts
Pipelines — Visualize NLP topologies, including embedding nodes and retriever logic
Run Search — Dispatch immediate LLM or Retriever invocations to test RAG pipelines
Document Management — List files and inspect metadata attached to source document embeddings
Vector Search — Trigger dense or sparse vector searches over indexed enterprise knowledge

The Haystack (deepset Cloud) MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Haystack (deepset Cloud) to LlamaIndex via MCP

Follow these steps to integrate the Haystack (deepset Cloud) MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from Haystack (deepset Cloud)

Why Use LlamaIndex with the Haystack (deepset Cloud) MCP Server

LlamaIndex provides unique advantages when paired with Haystack (deepset Cloud) through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Haystack (deepset Cloud) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Haystack (deepset Cloud) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Haystack (deepset Cloud), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Haystack (deepset Cloud) tools were called, what data was returned, and how it influenced the final answer

Haystack (deepset Cloud) + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Haystack (deepset Cloud) MCP Server delivers measurable value.

Hybrid search: combine Haystack (deepset Cloud) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Haystack (deepset Cloud) to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Haystack (deepset Cloud) for fresh data

Analytical workflows: chain Haystack (deepset Cloud) queries with LlamaIndex's data connectors to build multi-source analytical reports

Haystack (deepset Cloud) MCP Tools for LlamaIndex (7)

These 7 tools become available when you connect Haystack (deepset Cloud) to LlamaIndex via MCP:

get_file

Get file metadata

get_pipeline

Get pipeline details

list_files

List uploaded files

list_pipelines

List pipelines

list_workspaces

List workspaces

run_pipeline

Run a pipeline search

search_documents

Search documents in index

Example Prompts for Haystack (deepset Cloud) in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Haystack (deepset Cloud) immediately.

"List all pipelines in my 'production' workspace"

"Run a search for 'AI security compliance' using the default-rag pipeline"

"List the files in my 'knowledge-base' workspace"

Troubleshooting Haystack (deepset Cloud) MCP Server with LlamaIndex

Common issues when connecting Haystack (deepset Cloud) to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Haystack (deepset Cloud) + LlamaIndex FAQ

Common questions about integrating Haystack (deepset Cloud) MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Haystack (deepset Cloud) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.