How to Use the Criteo Marketing API MCP in LangChain

Give your LangChain agents direct access to Criteo budgets and ad sets using this MCP Server.

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Connect Criteo Marketing API MCP to LangChain

Create your Vinkius account to connect Criteo Marketing API 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 Criteo Marketing API with LangChain

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Chain Criteo data directly into reasoning loops

This MCP Server connects your LangChain agents to Criteo campaign data so they can make smart decisions. By exposing these tools to your graph, the agent can call `list_marketing_campaigns` to find active runs, then instantly pipe those IDs into `get_campaign_details` without manual coding. This turns static API calls into dynamic reasoning chains. The output of one tool immediately feeds the next, letting your agent evaluate performance and adjust tactics on the fly.

Track Criteo MCP Server calls in LangChain

This server integrates with LangSmith to trace every single call to `get_marketing_statistics` and `list_marketing_budgets`. Debugging multi-step marketing workflows is a nightmare when you cannot see what your agent is doing. You get complete visibility into latency, token spend, and exact payloads. No more guessing why an agent decided to pause a budget or query a specific ad set.

Build autonomous budget allocators with LangChain

This MCP Server enables autonomous budget allocators to run inside your LangChain loops. Stop manually shifting cash between ad sets. Let your agent run a loop that checks `list_marketing_budgets`, grabs the current performance metrics using `get_marketing_statistics`, and flags underperforming segments. Because LangChain handles state and memory, your agent remembers past runs. It can compare yesterday's performance against today's before recommending budget shifts.

Setup guide

Set up Criteo Marketing API 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 Criteo Marketing API 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({
    "criteo-marketing-api-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 Criteo Marketing API 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 Criteo Marketing. 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 Criteo Marketing API MCP in LangChain

You install the LangChain MCP adapter and spin up the client pointing to the Vinkius endpoint. From there, you call get_tools and pass them straight to your agent constructor.

Yes, you can configure standard LangChain runnable retries around the tool calls. This keeps your chains from breaking when pulling heavy statistics.

Yes. Every tool execution is treated as a standard runnable step. You will see inputs and outputs for statistics and budgets directly in your LangSmith dashboard.

You can register multiple servers with the multi-server client. Your agent can pull data from a database and use it to filter Criteo audiences in the same run.

Vinkius runs this server inside an isolated sandbox. Your raw API keys and Criteo budget details are never stored or exposed to the LLM provider, protecting your financial data.

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