How to Use the Zhumu / 瞩目 MCP in LangChain

Q: How do I search for past Zhumu / 瞩目 information using LangChain?

Your AI client indexes the output of tools like getaccountreport into your chain. You can query historical usage data, letting you find specific metrics from previous months without writing custom SQL.

Q: Can I check all upcoming Zhumu / 瞩目 sessions using LangChain?

Yep. The listmeetings tool is exactly for that. Your agent calls it, and you get a clean list of every scheduled event in the system.

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Connect Zhumu / 瞩目 MCP to LangChain

Create your Vinkius account to connect Zhumu / 瞩目 to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Manage all user and meeting data

The `get_user` tool pulls specific account details, while the `list_users` tool gives you a full directory of people in your organization. You can then use these user IDs when running actions like `create_meeting`, making sure everyone invited is active.

Schedule and modify sessions

You call `list_meetings` to see what’s coming up, or you check the specifics using `get_meeting`. Need a change? The `update_meeting` tool lets your agent adjust settings on existing events. This sequence allows for complex scheduling logic—check availability first, then modify.

Track platform usage and content

For auditing purposes, the `get_account_report` provides detailed usage metrics that aren't available anywhere else. You can also manage archived video material by listing recordings via `list_recordings`. This pairing lets your agent confirm both how much was used and what specific data needs to be retrieved.

Setup guide

Set up Zhumu / 瞩目 MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Zhumu / 瞩目 tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "zhumu-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Zhumu / 瞩目 transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Your AI client indexes the output of tools like `get_account_report` into your chain. You can query historical usage data, letting you find specific metrics from previous months without writing custom SQL.

Absolutely. Since the output of one tool becomes the input for the next, you build true reasoning pipelines. For instance, find a user ID first, then use that ID to update their meeting settings.

The server handles sensitive communication metadata, specifically the structure and details of meetings (meeting IDs, dates, times). It's important your agent respects these boundaries.

Yep. The `list_meetings` tool is exactly for that. Your agent calls it, and you get a clean list of every scheduled event in the system.

Your client supports passing MCP tools to external databases or vector stores. This means you can combine Zhumu's API data with your company’s internal knowledge base in one chain.

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