How to Use the AdButler MCP in LangChain

Q: Can a LangChain agent make decisions using AdButler metrics?

Yes. The agent calls getperformancereport to fetch CTR and impressions, analyzes the numbers, and feeds that output into the next tool in your chain automatically.

Q: Does LangSmith trace my AdButler tool calls in LangChain?

It does. Every time your LangChain agent invokes listcampaigns or listzones, LangSmith records the latency, inputs, and raw JSON outputs for debugging.

Connect the AdButler MCP Server directly to your LangChain reasoning chains to run live campaign reporting and inventory checks.

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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

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Claude

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Automated performance audits with LangChain chains

`get_performance_report` pulls raw impressions, clicks, and CTR data straight into your active run. Your agent reads these metrics through the MCP tool, evaluates performance against your targets, and instantly decides whether to pull creative assets or flag a low-performing zone. Because this runs as a step in your chain, you do not copy-paste data between dashboards. LangSmith traces the entire tool call, tracking exactly how many tokens your agent used to analyze the AdButler metrics.

Inventory and zone mapping

`list_zones` and `list_publishers` let your agent map out your available ad inventory on the fly. Pass a publisher ID from a previous step, and the agent immediately finds every active zone placement linked to that publisher. You build workflows where the agent checks zone availability first, then grabs campaign creatives using `list_creatives` to match the right banner size. No manual mapping files needed.

Live campaign discovery

`list_campaigns` exposes your active and pending self-serve ad campaigns to your agent. The agent scans the list, identifies pending items that need approval, and passes that list to the next chain link. This turns static ad management into a dynamic pipeline. Your agent handles the discovery work, leaving you to write the logic that flags anomalies or sends Slack alerts.

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-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)

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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 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.

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Common questions about AdButler MCP in LangChain

Install the adapter with pip install langchain-mcp-adapters langgraph. Use MultiServerMCPClient pointing to the Vinkius URL, call get_tools(), and pass them directly to your agent constructor.

Yes. The agent calls get_performance_report to fetch CTR and impressions, analyzes the numbers, and feeds that output into the next tool in your chain automatically.

It does. Every time your LangChain agent invokes list_campaigns or list_zones, LangSmith records the latency, inputs, and raw JSON outputs for debugging.

Yes, the client supports multi-server aggregation. You can combine this ad server with database or Slack tools in a single agent session.

Vinkius runs the server in an isolated, zero-trust V8 sandbox. Your campaign details, creative assets, and performance reports are never stored, and your API keys are handled securely through a single endpoint token.

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