How to Use the Zhumu / 瞩目 MCP in CrewAI

Q: Can CrewAI perform user lookups on Zhumu / 瞩目?

Yes, a dedicated Directory Agent runs listusers. It then passes IDs to the getuser tool to gather specific profile data needed for an operation.

Q: What is the scope of Zhumu / 瞩目 reporting with CrewAI?

The agents can run getaccountreport to get usage metrics. This report output serves as shared memory, allowing subsequent agents to make decisions based on resource availability.

Q: Does CrewAI handle Zhumu / 瞩目 webinar listings?

The Content Agent uses listwebinars and listrecordings. This lets the team build autonomous pipelines that track all educational content over time.

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Connect Zhumu / 瞩目 MCP to CrewAI

Create your Vinkius account to connect Zhumu / 瞩目 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 Zhumu / 瞩目 with CrewAI

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The Scheduling Agent handles calendar events.

You assign the `list_meetings` tool to a dedicated agent. This agent can monitor and report on all upcoming sessions, acting as a centralized scheduler. Another specialized agent uses `create_meeting` or `update_meeting` when manual intervention is needed, completing the cycle from monitoring to action.

The Directory Agent manages user accounts.

A dedicated Directory Agent runs `list_users` to build a complete map of the organization's users. This agent then uses `get_user` to pull detailed information about specific employees. If the team needs billing insight, they can delegate the `get_account_report` call to this specialized agent.

The Content Agent archives and cleans records.

This agent monitors historical data by calling `list_recordings` or `list_webinars`. If content needs removal, it uses the `delete_meeting` tool to clean up old entries. It also tracks current sessions using `list_meetings`, ensuring that all relevant documentation is available for analysis.

Setup guide

Set up Zhumu / 瞩目 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 Zhumu / 瞩目 tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Zhumu / 瞩目 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 Zhumu / 瞩目. 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 Zhumu / 瞩目 MCP in CrewAI

CrewAI assigns specific roles to agents. One agent handles the scheduling using `list_meetings`, while another uses `get_meeting` to pull details for analysis.

Yes, a dedicated Directory Agent runs `list_users`. It then passes IDs to the `get_user` tool to gather specific profile data needed for an operation.

The agents can run `get_account_report` to get usage metrics. This report output serves as shared memory, allowing subsequent agents to make decisions based on resource availability.

The Content Agent uses `list_webinars` and `list_recordings`. This lets the team build autonomous pipelines that track all educational content over time.

It exposes user profiles, account reports, scheduling details, and historical recordings. The framework ensures these diverse data types are passed between specialized agents for processing.

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