How to Use the Amazon Ads MCP in LangChain

Q: How do I pass Amazon Ads tools to a LangChain agent?

Install langchain-mcp-adapters and connect to the server URL using the MultiServerMCPClient. Call client.gettools() to retrieve the Amazon Ads tools and pass them directly to your createagent call.

Q: Can LangChain run multi-step Amazon Ads report checks?

Yes, you can build a sequential chain that first triggers requestspreport and then loops getreportstatus until the file is ready. LangChain handles the state between these tool calls automatically.

Q: How does LangChain handle multiple Amazon Ads profiles?

The agent calls listprofiles first to discover active advertiser profiles. You can then configure your LangChain chain to pass the selected profile ID as an input parameter for subsequent campaign queries.

Q: What is the setup for this MCP Server in LangGraph?

You initialize the connection using MultiServerMCPClient pointing to the Vinkius endpoint. Wrap the tools using the adapter, then register them as nodes inside your LangGraph state graph.

Run multi-step ad optimization chains in LangChain using live performance data from your Amazon Ads account.

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Connect Amazon Ads MCP to LangChain

Create your Vinkius account to connect Amazon Ads 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 Amazon Ads with LangChain

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Build LangChain agents that audit Sponsored Products

Your ReAct agent can now inspect your actual campaign architecture without you writing manual API boilerplate. The agent starts by calling `list_profiles` to map your accounts, then pulls active campaigns using `list_sp_campaigns` and target groups with `list_sp_ad_groups` to find structural gaps. Because this runs inside a LangChain runnable, you can feed these campaign structures straight into a custom prompt template. You get a clear, step-by-step diagnostic of your ad groups without leaving your Python run loop.

Trace Amazon Ads report pipelines with LangSmith

Debugging broken reporting pipelines is a nightmare. This MCP Server lets you request detailed performance data via `request_sp_report` and monitor the entire execution chain inside LangSmith. You can track exactly how long `get_report_status` takes to return a ready state during execution. If an agent fails to fetch the report, you see the exact payload and token usage in your tracing dashboard instantly.

Chain search keyword updates to performance metrics

Stop manually comparing spreadsheets to adjust your ad bids. Use a LangChain agent to fetch your current target list via `list_sp_keywords` and `list_sb_keywords` in a single sequence. The agent parses the keyword lists, evaluates them against your custom logic, and prepares the next step in your chain. You combine these live Amazon Ads tool outputs with other database integrations using this MCP Server.

Setup guide

Set up Amazon Ads 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 Amazon Ads 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({
    "amazon-ads-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 Amazon Ads 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 Amazon Ads. 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 Amazon Ads MCP in LangChain

Install `langchain-mcp-adapters` and connect to the server URL using the `MultiServerMCPClient`. Call `client.get_tools()` to retrieve the Amazon Ads tools and pass them directly to your `create_agent` call.

Yes, you can build a sequential chain that first triggers `request_sp_report` and then loops `get_report_status` until the file is ready. LangChain handles the state between these tool calls automatically.

The agent calls `list_profiles` first to discover active advertiser profiles. You can then configure your LangChain chain to pass the selected profile ID as an input parameter for subsequent campaign queries.

You initialize the connection using `MultiServerMCPClient` pointing to the Vinkius endpoint. Wrap the tools using the adapter, then register them as nodes inside your LangGraph state graph.

Your advertiser profile IDs and Sponsored Products campaign metrics never persist on our servers. Vinkius runs the adapter in an ephemeral, zero-trust V8 sandbox that destroys the runtime instance the moment your tool execution finishes.

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