How to Use the AdButler MCP in LangChain

Q: Can LangChain debug my AdButler campaigns?

Yes, your agent can call listbanners and listzones to find issues. It then reports back with specific details on what looks off in your setup.

Build multi-step reasoning chains in LangChain that pull live ad statistics and campaign data directly from AdButler.

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Connect AdButler MCP to LangChain

Create your Vinkius account to connect AdButler 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 AdButler with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain AdButler logic in LangChain

Your agent runs `get_stats` to grab current impression data and feeds those numbers immediately into your next logic block. The output of one tool becomes the input for the next step in your chain. This MCP Server keeps your pipeline moving without manual intervention. You get full visibility into every tool call using LangSmith tracing to see exactly what your agent decided.

Automate campaign management

Feed `list_campaigns` results into a decision node to prioritize high-performing zones. Your agent evaluates the data before it touches your live configuration. Everything stays in the loop. You define the logic, and the agent handles the heavy lifting of parsing ad inventory across your properties.

Query AdButler records

Your agent uses `get_advertiser` or `list_publishers` to pull contact details during a conversation. It reads the raw API response and summarizes it for your review. This keeps your state clean. You avoid duplicate entries by having the agent check existing records before creating new ones in the AdButler dashboard.

Setup guide

Set up AdButler 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 AdButler 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({
    "adbutler-alternative-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 AdButler transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AdButler. 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

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

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Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about AdButler MCP in LangChain

Install the necessary adapters and initialize the MultiServerMCPClient with your endpoint. This lets your agent call any of the ten available tools directly from your code.

Yes, your agent can call `list_banners` and `list_zones` to find issues. It then reports back with specific details on what looks off in your setup.

Use the client session feature to maintain context between steps. Your agent keeps track of previous tool outputs so your reasoning remains consistent.

The server connects to the specific instance you configure. You can manage multiple endpoints by setting up separate server instances within your pipeline.

Vinkius uses a secure, ephemeral sandbox for every request. We only process advertiser details, campaign stats, and zone configurations through your authorized token.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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