How to Use the Aptabase (Mobile Analytics) MCP in LangChain

Q: How does LangSmith trace calls made to the Aptabase (Mobile Analytics) MCP Server in LangChain?

Every execution of sendevent shows up as a tool run in your LangSmith dashboard. You can inspect the exact payload, execution time, and success status of each telemetry call.

Q: What is the setup process for this MCP Server in a Python environment?

Run pip install langchain-mcp-adapters langgraph first. Then, establish a connection to the HTTP endpoint using the MultiServerMCPClient class.

Feed mobile analytics events straight from your LangChain reasoning loops into Aptabase.

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Connect Aptabase (Mobile Analytics) MCP to LangChain

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Setup Aptabase (Mobile Analytics) with LangChain

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Chain custom telemetry with LangChain agents

The `send_event` tool lets your LangChain agent post custom telemetry events directly to your Aptabase dashboard. When a chain finishes a complex run, the agent automatically triggers this endpoint to record execution metrics. This means you track actual user outcomes inside your mobile app analytics without writing manual logging blocks. You configure the agent to pass token usage or user feedback directly into the event payload.

LangSmith tracing for Aptabase MCP Server runs

This MCP Server integrates with LangChain's observability tools so you can trace every single `send_event` call in LangSmith. You see the exact payload structure, latency, and response status of your mobile telemetry events during execution. If a telemetry dispatch fails, you debug the input arguments directly within the LangSmith UI. It keeps your mobile event pipeline reliable without guessing what the agent sent.

Multi-step telemetry workflows in LangGraph

Using the `send_event` tool inside a multi-step LangGraph pipeline lets you record events after specific agent decisions. Your agent evaluates the user's intent, runs its logic, and then logs the final state. You get clean, structured telemetry in Aptabase that matches the exact paths your graph took. There is no need to write custom interceptors when the agent handles the reporting natively.

Setup guide

Set up Aptabase (Mobile Analytics) 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 Aptabase (Mobile Analytics) 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({
    "aptabase-mobile-analytics-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 Aptabase (Mobile Analytics) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Aptabase (Mobile Analytics) MCP in LangChain

Pass the properties as a key-value dictionary directly to the `send_event` tool within your chain. Your LangChain agent formats these properties dynamically based on the conversation history before dispatching.

Yes, you initialize the client with the HTTP URL provided by Vinkius. Once registered, the `send_event` tool becomes available alongside other tools in your agent's pool.

Every execution of `send_event` shows up as a tool run in your LangSmith dashboard. You can inspect the exact payload, execution time, and success status of each telemetry call.

Run `pip install langchain-mcp-adapters langgraph` first. Then, establish a connection to the HTTP endpoint using the MultiServerMCPClient class.

Yes, the server operates in a zero-trust sandbox that only processes the telemetry event names and custom properties you explicitly send. Vinkius secures the endpoint token so your Aptabase keys are never exposed to the client.

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