How to Use the Criteo Marketing API MCP in CrewAI

Q: How do I connect the Criteo Marketing API to CrewAI?

Install crewaitools via pip. Pass the Vinkius endpoint directly into your agent's mcps array. The framework will automatically discover all ten available tools.

Q: Can I restrict which Criteo Marketing API tools a CrewAI agent can use?

Yes, use MCPServerHTTP from crewai.mcp and apply a toolfilter. You can give your analyst agent access to stats while restricting the auditor agent to campaign details.

Deploy a CrewAI agent team to autonomously monitor Criteo campaigns, audit ad sets, and analyze performance.

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Connect Criteo Marketing API MCP to CrewAI

Create your Vinkius account to connect Criteo Marketing API to CrewAI 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 CrewAI

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Run Autonomous Performance Reviews via MCP Server

Assign an analyst agent to monitor your daily spend. This agent uses `get_marketing_statistics` and `list_marketing_budgets` to track pacing across all active accounts without you lifting a finger. While the analyst crunches the numbers, a reporting agent formats the findings. The multi-agent setup means one script handles the raw data extraction while another figures out what the metrics actually mean for your business.

Audit Campaign and Ad Set Configurations

Misconfigured targeting drains money fast. You can task a compliance agent with running `list_marketing_campaigns` followed by `get_campaign_details` to check every single setting. If the campaign looks fine, the agent digs deeper into the groups. It pulls `list_ad_sets` and `get_ad_set_details` to verify the bidding strategies and placement rules match your company guidelines.

Analyze Creatives and Audiences Together

Give your research agent the job of finding audience overlap. By executing `list_audiences` and `list_audience_segments`, the crew builds a map of who is seeing what. The team then cross-references those segments with `list_ad_creatives`. You get a complete breakdown of which banners are hitting which demographics, all generated sequentially by specialized agents.

Setup guide

Set up Criteo Marketing API MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Criteo Marketing API tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Criteo Marketing API Analyst",
    goal="Access and analyze Criteo Marketing API data via MCP.",
    backstory="Expert analyst with direct Criteo Marketing API access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Criteo Marketing API transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Criteo Marketing API Analyst",
        goal="Access and analyze Criteo Marketing API data via MCP.",
        backstory="Expert analyst with direct Criteo Marketing API access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Criteo Marketing API transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Install `crewai[tools]` via pip. Pass the Vinkius endpoint directly into your agent's `mcps` array. The framework will automatically discover all ten available tools.

Yes, use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. You can give your analyst agent access to stats while restricting the auditor agent to campaign details.

The framework supports stdio, SSE, and Streamable HTTP. For Vinkius-hosted remote servers, HTTP or SSE are your best bets for stable multi-agent execution.

CrewAI handles shared memory automatically. Once your data-gathering agent pulls the active budgets, the reasoning agents can access those numbers in subsequent tasks.

Nobody but your connected client. Vinkius spins up an isolated sandbox for your request, processes the performance metrics, and immediately destroys the environment. Zero persistent storage.

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