How do I find my Zhumu App Key and Secret?

Log in to the Zhumu Developer Center, create a new application, and you will find your unique App Key and App Secret in the application credentials section.

Can I retrieve meeting recordings through this server?

Yes. Use the `list_recordings` tool with a specific user ID to retrieve a list of all cloud recordings saved for that user's sessions.

Is it possible to manage webinars?

Yes! Use the `list_webinars` tool to retrieve a list of all scheduled webinars and identify upcoming sessions and engagement metrics.

Zhumu / 瞩目 MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Zhumu / 瞩目
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Zhumu / 瞩目 through Vinkius, pass the Edge URL in the `mcps` parameter and every Zhumu / 瞩目 tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Zhumu / 瞩目 Specialist",
    goal="Help users interact with Zhumu / 瞩目 effectively",
    backstory=(
        "You are an expert at leveraging Zhumu / 瞩目 tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Zhumu / 瞩目 "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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About Zhumu / 瞩目 MCP Server

Empower your AI agent to orchestrate your video collaboration with Zhumu (瞩目), the premier cloud meeting platform in China. By connecting Zhumu to your agent, you transform complex meeting scheduling, user auditing, and recording management into a natural conversation. Your agent can instantly list upcoming meetings, retrieve detailed participant information, monitor cloud recordings, and even schedule new sessions without you ever needing to navigate the comprehensive Zhumu portal. Whether you are conducting a cross-functional team sync or coordinating a large-scale webinar, your agent acts as a real-time collaboration assistant, keeping your schedule accurate and your meetings organized.

When paired with CrewAI, Zhumu / 瞩目 becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Zhumu / 瞩目 tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Meeting Orchestration — List, retrieve, and schedule video meetings with full support for topics and timing.
User Auditing — Browse and retrieve detailed user profiles across your organization.
Recording Control — List and access cloud recordings for past sessions to ensure knowledge sharing.
Webinar Monitoring — Monitor scheduled webinars and participant engagement levels.
Usage Insights — Retrieve high-level account reports and activity summaries for your collaboration environment.

The Zhumu / 瞩目 MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Zhumu / 瞩目 to CrewAI via MCP

Follow these steps to integrate the Zhumu / 瞩目 MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Zhumu / 瞩目

Why Use CrewAI with the Zhumu / 瞩目 MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Zhumu / 瞩目 through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Zhumu / 瞩目 + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Zhumu / 瞩目 MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Zhumu / 瞩目 for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Zhumu / 瞩目, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Zhumu / 瞩目 tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Zhumu / 瞩目 against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Zhumu / 瞩目 MCP Tools for CrewAI (10)

These 10 tools become available when you connect Zhumu / 瞩目 to CrewAI via MCP:

create_meeting

Create a new meeting

delete_meeting

Delete a meeting

get_account_report

Get usage reports

get_meeting

Get meeting details

get_user

Get user details

list_meetings

List upcoming meetings

list_recordings

List cloud recordings

list_users

List organization users

list_webinars

List scheduled webinars

update_meeting

Update meeting settings

Example Prompts for Zhumu / 瞩目 in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Zhumu / 瞩目 immediately.

"List all my upcoming meetings in Zhumu."

"Schedule a meeting titled 'Design Feedback' for today."

"Show me the last 5 cloud recordings."

Troubleshooting Zhumu / 瞩目 MCP Server with CrewAI

Common issues when connecting Zhumu / 瞩目 to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Zhumu / 瞩目 + CrewAI FAQ

Common questions about integrating Zhumu / 瞩目 MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.