How to Use the WeCom / 企业微信 MCP in CrewAI

Q: How do I manage WeCom / 企业微信 users with CrewAI?

You can use listusers and then filter that list using tags via gettagusers. The agents collaborate: one agent pulls the raw data, and another specializes in filtering based on your rules.

Q: What data types does this WeCom / 企业微信 MCP Server touch when used with CrewAI?

The server accesses user records (getuser), department lists (listdepartments), and tag associations. The agents read these diverse datasets to complete their assigned roles.

Q: If I need to send a message about WeCom / 企业微信 data using CrewAI, how is it done?

You assign the sendmessage tool to an 'Action Agent.' This agent waits until all other tasks are complete and then sends the final status report via the tool.

Run multi-agent WeCom / 企业微信 operations with CrewAI for autonomous task completion.

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Connect WeCom / 企业微信 MCP to CrewAI

Create your Vinkius account to connect WeCom / 企业微信 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 WeCom / 企业微信 with CrewAI

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Managing User Groups in CrewAI

Need to find users based on criteria? The `get_tag_users` tool pulls all user lists matching a specific tag from WeCom / 企业微信. A specialized 'Research Agent' can use this data, while an 'Analysis Agent' summarizes the findings. CrewAI lets you run multiple agents (e.g., one gets tags, another checks departments) that collaborate on complex tasks without constant human oversight.

Retrieving Directory Info with CrewAI

A 'Directory Agent' can use `list_users` to pull all members of a department. This agent then passes the list to a 'Moderator Agent' which might cross-reference that data against department details retrieved by `get_department`. This role-based approach means one agent handles the raw listing, and another handles the contextual cleanup.

Reporting Status via MCP Server

The 'Action Agent' uses `send_message` to report final status updates into WeCom / 企业微信. This is useful for multi-step processes where multiple agents finish their tasks and need a single agent to confirm the outcome. This setup guarantees that even if research or analysis fails, there's always a mechanism to send a failure notification.

Setup guide

Set up WeCom / 企业微信 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 WeCom / 企业微信 tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent WeCom / 企业微信 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 WeCom / 企业微信. 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 WeCom / 企业微信 MCP in CrewAI

You can use `list_users` and then filter that list using tags via `get_tag_users`. The agents collaborate: one agent pulls the raw data, and another specializes in filtering based on your rules.

Yes. You can build full autonomous pipelines. For instance, Agent A lists departments, Agent B gets users for each department, and a final agent summarizes the findings—all without you manually running the steps.

The server accesses user records (`get_user`), department lists (`list_departments`), and tag associations. The agents read these diverse datasets to complete their assigned roles.

You assign the `send_message` tool to an 'Action Agent.' This agent waits until all other tasks are complete and then sends the final status report via the tool.

You can set up a workflow where an agent checks for missing data (e.g., `list_departments` returns empty). If so, the moderator agent escalates the task or triggers a manual alert instead of failing silently.

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