How to Use the Digital Turbine MCP in LangChain

Q: Can my LangChain agent track Digital Turbine revenue reports?

Yes, the agent calls getrevenuereport to pull data for specific date ranges. It processes the output as part of your chain, making the revenue numbers available for further analysis or logging.

Chain Digital Turbine data directly into your LangChain agents to automate mobile growth workflows without manual API polling.

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Connect Digital Turbine MCP to LangChain

Create your Vinkius account to connect Digital Turbine 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 Digital Turbine with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain performance data into reasoning loops

Feed your agent live metrics using `get_performance_summary` to trigger automated budget adjustments. You define the logic, and the agent executes based on real-time install and CTR data. This MCP Server fits into your LangChain pipelines by turning static reports into actionable steps. Every tool call flows into the next node of your chain for consistent decision-making.

Automate campaign discovery and management

Your agent can identify high-value opportunities by chaining `list_growth_campaigns` with `search_campaigns_by_name`. It filters through your account inventory to find exactly what needs attention. Stop digging through dashboards. Let your agent handle the heavy lifting of sorting through active acquisition channels while you focus on the strategy.

Monitor monetization status programmatically

Use `get_app_monetization_details` to verify settings across your portfolio. Your LangChain agent checks these configurations during every run to ensure your monetization strategy remains aligned with your goals. This integration allows for immediate status updates. You get full visibility into app health without writing custom API wrappers.

Setup guide

Set up Digital Turbine 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 Digital Turbine 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({
    "digital-turbine-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 Digital Turbine transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Digital Turbine MCP in LangChain

You install the adapters and initialize the client pointing to your server endpoint. Once the tools are registered, you pass them directly to your agent's tool list for immediate execution.

Yes, the agent calls `get_revenue_report` to pull data for specific date ranges. It processes the output as part of your chain, making the revenue numbers available for further analysis or logging.

The server operates within an ephemeral V8 sandbox. Your sensitive API tokens stay local, and only the specific data requested through the MCP standard is transmitted to your agent.

LangChain handles the error based on your defined recovery logic. You can set the agent to retry, skip the step, or alert you if the API returns an unexpected status code.

The server manages the communication bridge, but you should implement backoff logic within your agent's chain. This ensures your calls stay within the API limits for your specific account.

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