How to Use the LlamaIndex (AI Data Framework & RAG) MCP in CrewAI

Q: Can CrewAI list projects from LlamaIndex (AI Data Framework & RAG)?

Yes, use the listprojects tool. Your agents can then iterate through these projects to gather information for complex research goals.

Deploy autonomous RAG research crews using LlamaIndex (AI Data Framework & RAG) and CrewAI.

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Connect LlamaIndex (AI Data Framework & RAG) MCP to CrewAI

Create your Vinkius account to connect LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) with CrewAI

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Perplexity

Coordinate specialized agents

Assign an agent to use `query_pipeline` while another agent uses `list_files` for verification. This keeps your research crew focused and efficient. One agent acts as the researcher, the other as the monitor. They share the MCP context to ensure the final output is accurate and based on real data.

Scale data discovery

Use `list_projects` to let your crew explore all available data sources autonomously. They can identify which index contains the answer without you pointing them to it. This makes your agents truly independent. They browse the available projects and pipelines, then decide which one to query based on the task objective.

Verify RAG accuracy

Have your moderator agent run `list_indexes` before any critical operation. It ensures the crew is working with the latest, valid data sets. If the index list changes, the agent detects it. This prevents the crew from wasting cycles on stale or incorrect RAG pipelines.

Setup guide

Set up LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent LlamaIndex (AI Data Framework & RAG) 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="LlamaIndex (AI Data Framework & RAG) Analyst",
        goal="Access and analyze LlamaIndex (AI Data Framework & RAG) data via MCP.",
        backstory="Expert analyst with direct LlamaIndex (AI Data Framework & RAG) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex. 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 LlamaIndex (AI Data Framework & RAG) MCP in CrewAI

Simply include the MCP server URL in your agent definition. The agent will then be able to call `query_pipeline` as part of its assigned tasks.

Yes, use the `list_projects` tool. Your agents can then iterate through these projects to gather information for complex research goals.

Agents receive the tool output as text in their memory. They analyze this content and use it to build their final reports or decisions.

CrewAI agents can report the error to the manager agent. You can then trigger a secondary agent to investigate the pipeline status.

The connection uses encrypted tokens. Your sensitive documents are only accessed during the query and are never persisted by the MCP server itself.

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