How to Use the Zilliz Cloud MCP in CrewAI

Q: How do I run a complex query with Zilliz Cloud and CrewAI?

A dedicated 'Research Agent' uses queryentities to fetch the relevant data. The results are then passed to another agent for analysis, enabling deep, multi-step operations that mimic human expertise.

Q: Can CrewAI manage multiple vector indexes with Zilliz Cloud?

Yes. Using listcollections, your crew can systematically assess all available collections. This allows the system to automatically route requests to the correct knowledge base for a comprehensive answer.

Autonomous operations: Running collaborative search tasks using CrewAI’s specialized agents.

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Connect Zilliz Cloud MCP to CrewAI

Create your Vinkius account to connect Zilliz 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 Zilliz Cloud with CrewAI

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Perplexity

Collaborative Vector Search with CrewAI

When Agent A needs context, it calls `search_vectors`. This result is passed to Agent B for analysis. The MCP Server allows multiple specialized roles—like a 'Data Researcher' and an 'Analyzer'—to collaborate on finding deep insights from Zilliz Cloud. The process uses the vector similarity search results as shared memory, ensuring every agent works off the same accurate data set.

Setup Vector Context in CrewAI

The 'Moderator Agent' can start by running `list_collections` to define the scope of work. It then uses `describe_collection` to validate the structure before instructing another agent to `create_collection` or prepare data via `insert_entities`. This controlled setup ensures that when any specialized agent runs a query, they're using a known and validated source.

Advanced Data Management for CrewAI

Autonomous operations require clean data. Agents can use `query_entities` to pull specific information based on metadata filters defined in their roles. They also have the option to `delete_entities` or even `drop_collection` if the research concludes the data is obsolete. This allows for full lifecycle management of vector collections without human intervention.

Setup guide

Set up Zilliz 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 Zilliz Cloud tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Zilliz 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="Zilliz Cloud Analyst",
        goal="Access and analyze Zilliz Cloud data via MCP.",
        backstory="Expert analyst with direct Zilliz Cloud access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Zilliz 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 Zilliz 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 Zilliz Cloud MCP in CrewAI

A dedicated 'Research Agent' uses `query_entities` to fetch the relevant data. The results are then passed to another agent for analysis, enabling deep, multi-step operations that mimic human expertise.

Yes. Using `list_collections`, your crew can systematically assess all available collections. This allows the system to automatically route requests to the correct knowledge base for a comprehensive answer.

The MCP Server operates in an isolated, zero-trust sandbox environment. Authentication is handled via an endpoint token, guaranteeing that even complex multi-agent pipelines remain highly secure.

The system can be configured to monitor the failure. A designated 'Escalation Agent' can then run alternative tools, like refreshing the data using `load_collection`, rather than simply stopping.

This server manages vector collections and entities. The specific data types involved are the vectors themselves, along with associated metadata used for filtering during searches or queries.

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