How to Use the Haystack (deepset Cloud) MCP in CrewAI

Q: How do I configure Haystack (deepset Cloud) for my CrewAI agents?

Pass the server URL into the mcps array of your agent definition. CrewAI will then automatically register the tools, allowing your agents to invoke them during their task execution.

Deploy specialized agent crews in CrewAI that collaborate on search and document analysis using Haystack (deepset Cloud).

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Connect Haystack (deepset Cloud) MCP to CrewAI

Create your Vinkius account to connect Haystack (deepset Cloud) to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Haystack (deepset Cloud) with CrewAI

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Collaborative search agent crews

Assign a dedicated researcher agent to trigger `search_documents` while another agent synthesizes the output. CrewAI handles the handoff, ensuring your team of agents works in sequence. This structure is better than a single agent trying to do everything. You get specialized roles that focus on specific parts of your RAG pipeline.

Autonomous RAG pipeline orchestration

Use `run_pipeline` to automate document processing across multiple agents. One agent can prepare the query, and the next can interpret the results from deepset Cloud. It creates a self-correcting loop. If one search fails to provide enough context, the next agent in the sequence can refine the parameters and run the pipeline again.

Resource management for agents

Use `list_workspaces` to give your agents context about where they are operating. They can check their environment settings before initiating any search task. This keeps your crews aligned with the right project data. Agents won't waste time searching the wrong index because they can verify their workspace first.

Setup guide

Set up Haystack (deepset Cloud) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Haystack (deepset Cloud) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Haystack (deepset Cloud) Analyst",
    goal="Access and analyze Haystack (deepset Cloud) data via MCP.",
    backstory="Expert analyst with direct Haystack (deepset Cloud) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Haystack (deepset Cloud) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Haystack (deepset Cloud) Analyst",
        goal="Access and analyze Haystack (deepset Cloud) data via MCP.",
        backstory="Expert analyst with direct Haystack (deepset Cloud) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Haystack (deepset Cloud) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by deepset Cloud. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Haystack (deepset Cloud) MCP in CrewAI

Pass the server URL into the `mcps` array of your agent definition. CrewAI will then automatically register the tools, allowing your agents to invoke them during their task execution.

Yes, you can use tool filtering to limit which agents have access to specific functions. You might give the researcher agent `search_documents` while keeping `list_workspaces` for a manager agent.

The results from your tools are passed into the agent's memory. This allows subsequent agents in your crew to see exactly what was found during the initial Haystack (deepset Cloud) search.

You manage access through your endpoint token. Since CrewAI runs the MCP server locally, your data transit is isolated to the connection between your crew and the deepset Cloud API.

The server provides access to document content and metadata. It only returns the information explicitly requested by your agent's query, ensuring your document index remains controlled.

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