How to Use the LlamaCloud (Managed RAG & Parsing) MCP in CrewAI

Q: How do I share LlamaCloud (Managed RAG & Parsing) data across my CrewAI agents?

Use shared memory to store the output of getparsingresult. This allows all agents in the crew to reference the extracted data.

Q: Can I use multiple projects in CrewAI?

You can use listprojects to switch between different workspaces, allowing your crew to handle distinct document sets for different clients.

Deploy autonomous research crews with LlamaCloud (Managed RAG & Parsing) MCP Server integration for CrewAI.

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Connect LlamaCloud (Managed RAG & Parsing) MCP to CrewAI

Create your Vinkius account to connect LlamaCloud (Managed RAG & Parsing) 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 LlamaCloud (Managed RAG & Parsing) with CrewAI

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Specialized parsing crews in CrewAI

Assign a dedicated agent to handle document ingestion. Use `create_parsing_upload` to feed raw files to your research agents. Your crew stays focused on analysis while the parsing agent manages the data flow. This separation of concerns keeps your agents efficient.

Audit document pipelines in CrewAI

Let your monitor agent check system status. Use `list_pipelines` to ensure the crew is pulling from the correct data source. Your moderator agent can call `get_pipeline` to verify settings before authorizing a high-stakes retrieval task. This prevents errors before they occur.

Track ingestion jobs in CrewAI

Maintain oversight of your autonomous operations. Use `list_parsing_jobs` to see which documents are currently being processed. Your agents can wait for a specific job to finish by polling the status. It keeps the crew synchronized without human intervention.

Setup guide

Set up LlamaCloud (Managed RAG & Parsing) 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 LlamaCloud (Managed RAG & Parsing) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent LlamaCloud (Managed RAG & Parsing) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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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="LlamaCloud (Managed RAG & Parsing) Analyst",
        goal="Access and analyze LlamaCloud (Managed RAG & Parsing) data via MCP.",
        backstory="Expert analyst with direct LlamaCloud (Managed RAG & Parsing) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent LlamaCloud (Managed RAG & Parsing) 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 LlamaCloud. 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 LlamaCloud (Managed RAG & Parsing) MCP in CrewAI

Use shared memory to store the output of `get_parsing_result`. This allows all agents in the crew to reference the extracted data.

Yes. You can provide the tools to your agents and define tasks that trigger parsing when new documents appear in your pipeline.

It fits perfectly. Your manager agent can delegate the parsing task to a specialized agent using these tools.

All document uploads are processed through secure API channels. No sensitive data is cached or kept by the MCP server after the job concludes.

You can use `list_projects` to switch between different workspaces, allowing your crew to handle distinct document sets for different clients.

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