How to Use the TopOn / Mobvista MCP in LangChain

Q: Does the MCP Server support advanced reporting for Mobvista with LangChain?

Absolutely. The getmintegralreporting tool provides deep performance insights, which LangChain can then use to trigger further analysis or data retrieval from other endpoints.

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Connect TopOn / Mobvista MCP to LangChain

Create your Vinkius account to connect TopOn / Mobvista 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 TopOn / Mobvista with LangChain

Ask AI about this MCP

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Claude

Perplexity

Chaining Ad Performance Data

The `get_mintegral_app_stats` tool starts the chain by pulling current app performance metrics. Your agent can then use that data to decide which placements need review, calling `list_topon_placements` next. This multi-step process lets you build complex reasoning where one result dictates the next step.

Aggregating Monetization Reports

Need a full picture of revenue? Start by getting an overall view with `get_monetization_summary`. Next, you can narrow the focus using `get_topon_reporting` or checking specific performance via `get_mintegral_reporting`. The agent treats these results as sequential inputs to build one cohesive financial report.

Listing and Analyzing Ad Assets

The process starts by listing available assets. You can call `list_topon_apps` to see which applications are active, then run `list_mintegral_offers` to check current ad inventory. Finally, the chain uses this context to fetch a specific waterfall using `get_topon_waterfall`, ensuring all components fit together.

Setup guide

Set up TopOn / Mobvista 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 TopOn / Mobvista 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({
    "topon-mobvista-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 TopOn / Mobvista 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 TopOn / Mobvista. 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 TopOn / Mobvista MCP in LangChain

LangChain treats every tool call as a link in a chain. You pass the results from one ad performance function, like `get_topon_reporting`, directly into another agent step for decision-making.

Yes. The framework lets you build agents that monitor real-time data. You can write a pipeline that checks current `get_mintegral_app_stats` and then alerts if key performance indicators drop.

This MCP Server touches ad performance metrics, including eCPM, app statistics, and placement details. You're in control of what data gets processed through your chains.

Absolutely. The `get_mintegral_reporting` tool provides deep performance insights, which LangChain can then use to trigger further analysis or data retrieval from other endpoints.

You build the logic step-by-step. For instance, you might first check `list_topon_apps`, and then pass that list to a second tool call that fetches related data.

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