How to Use the Adobe Analytics MCP in LangChain

Build multi-step reasoning chains in LangChain using live data from your Adobe Analytics report suites.

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Connect Adobe Analytics MCP to LangChain

Create your Vinkius account to connect Adobe Analytics 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 Adobe Analytics with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Adobe Analytics tools in LangChain

Feed your `get_report` output directly into other LangChain nodes. You stop guessing what happened and start building pipelines where one tool result triggers the next step automatically. Your agent executes `list_report_suites` first to identify targets, then chains those IDs into metric lookups. This keeps your logic tight and your data flow visible in LangSmith.

Dynamic agent decision making

Let your agent decide whether to pull `get_dimensions` or `get_metrics` based on the user prompt. It evaluates the context and picks the right tool to satisfy the request. This MCP Server provides the raw data your agent needs to reason through complex questions. It doesn't just return static text; it gives your chain the specific numbers required for the next task.

Auditing data with LangChain

Use `list_segments` to pull audience data directly into your reasoning loop. You can verify segment definitions before running full reports. Your agent handles the heavy lifting of parsing JSON outputs from these tools. It turns raw API responses into actionable insights without manual intervention.

Setup guide

Set up Adobe 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 Adobe 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({
    "adobe-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 Adobe Analytics 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 Adobe Analytics. 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.

Why Choose Vinkius

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Common questions about Adobe Analytics MCP in LangChain

Install the MCP adapters and pass the tool definitions to your agent constructor. The agent will then call `get_report` whenever the conversation requires specific analytics data.

Yes, you link tools together so that the output of one function becomes the input for the next. This allows for complex workflows like listing suites, identifying a segment, and pulling a report in one go.

Any metric defined in your report suite is accessible via the `get_metrics` tool. Your agent can list these dynamically to ensure it uses the correct IDs for report requests.

Use the client session object to maintain state across multiple interactions. This keeps your connection to the API active while your agent processes long-running chains.

The server transmits only the specific report parameters you request over an encrypted connection. Vinkius manages your authentication token securely, ensuring your raw credentials never touch your agent's local memory.

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