How to Use the Umeng / 友盟+ MCP in LangChain

Q: How can I audit past Umeng / 友盟+ campaigns using LangChain?

Your agent can first use listpushtasks to see a roster of recent activity. Then, it checks the status of those tasks with getpushstatus. This gives you an auditable chain of records showing what was sent and when.

Q: How do I manage Umeng / 友盟+ messaging with LangChain?

You can execute full campaigns by first listing tasks (listpushtasks), then calling sendpush, and finally monitoring the job completion using getpushstatus. It keeps your entire communication process visible.

Build multi-step marketing workflows for Umeng / 友盟+ using LangChain.

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Connect Umeng / 友盟+ MCP to LangChain

Create your Vinkius account to connect Umeng / 友盟+ 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 Umeng / 友盟+ with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Manage Push Campaigns with MCP Server

You can build a campaign flow that first checks the current status of tasks using `get_push_status`. After verifying the system is ready, your agent can then send targeted messages via `send_push` or even cancel pending work with `cancel_push`. This sequence ensures you don't waste resources sending notifications if an existing job is already running.

Analyze User Growth and Health

Need to know if your push efforts are hitting the right people? Your agent can gather real-time data on new signups using `get_new_users` and track long-term engagement with `get_retention`. Combining these two steps lets you immediately see how many users were acquired versus how many stayed active.

Deep Dive into App Performance Data

Getting a high level view isn't enough. You can chain together calls to get the overall summary (`get_app_summary`), pinpoint where people spend their time using `get_duration_stats`, and gather specific user actions with `get_event_data`. This lets your agent build a comprehensive picture of product usage.

Setup guide

Set up Umeng / 友盟+ 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 Umeng / 友盟+ 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({
    "umeng-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 Umeng / 友盟+ 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 Umeng / 友盟+. 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 Umeng / 友盟+ MCP in LangChain

Your agent can first use `list_push_tasks` to see a roster of recent activity. Then, it checks the status of those tasks with `get_push_status`. This gives you an auditable chain of records showing what was sent and when.

Absolutely. Because the output of one tool call becomes the input for the next, your agent can process multi-step logic. You're not limited to simple calls; you're building reasoning pipelines.

The MCP Server exposes several key performance indicators, including user counts (`get_active_users`), new registrations, and detailed retention statistics. You get access to the core data needed for marketing decisions.

You can execute full campaigns by first listing tasks (`list_push_tasks`), then calling `send_push`, and finally monitoring the job completion using `get_push_status`. It keeps your entire communication process visible.

This server handles user behavioral metrics, specifically custom event data which can be retrieved using `get_event_data`.

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