How to Use the Walmart Connect Ads MCP in AutoGen

Achieve consensus on ad strategy with AutoGen and the Walmart Connect Ads MCP Server.

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Connect Walmart Connect Ads MCP to AutoGen

Create your Vinkius account to connect Walmart Connect Ads to AutoGen 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 Walmart Connect Ads with AutoGen

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Debating Campaign Changes via MCP Server

Need to increase spending? Your AI client deploys multiple agents that debate the plan. One agent might suggest using `ads_add_keywords` with higher bids, while a 'Budget Agent' reviews current spend via `ads_get_cpc_metrics`. They negotiate until they agree on a safe adjustment for `ads_update_daily_budget`. It’s decision-making through consensus. The system doesn't just execute; it argues and refines the plan.

Reviewing Performance with AutoGen Agents

When reviewing ad performance, multiple agents collaborate. One agent retrieves the data using `ads_get_item_health_report`, while another compares that snapshot against existing campaign structures retrieved via `ads_list_campaigns`. They debate whether the observed dip is due to a poor keyword choice or an overall budget issue. The final output isn't just a report; it’s a consensus diagnosis.

Strategic Keyword Management with AutoGen

Developing new keywords requires deliberation. An 'Opportunity Agent' uses `ads_get_keyword_performance` to spot gaps, while the 'Compliance Agent' checks potential bid levels using `ads_add_keywords`. They challenge each other on match types and optimal bids before recommending a final set of additions. This multi-perspective approach ensures that every ad change is vetted.

Setup guide

Set up Walmart Connect Ads MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Walmart Connect Ads tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Walmart Connect Ads_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Walmart Connect Ads data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Walmart Connect Ads_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Walmart Connect Ads data")
    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 Walmart Connect. 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 Walmart Connect Ads MCP in AutoGen

You build systems where multiple agents interact. For example, one agent checks the budget using `ads_get_cpc_metrics`, another proposes a change (`ads_update_daily_budget`), and they debate until the action is finalized.

Unlike simple tool calls, AutoGen forces agents to talk to each other. If a campaign needs pausing (`ads_pause_campaign`), one agent might flag the risk, and another debates if the pause is necessary based on performance data.

Absolutely. The system keeps a record of the conversation and deliberation process. You can review *why* an action was taken—for instance, reviewing the debate over whether to create a new ad group using `ads_create_ad_group`.

The agents tackle this in phases. One agent finds underperforming keywords via `ads_get_keyword_performance`, another proposes new replacement bids, and a third validates those bids against current campaign rules.

This server touches campaign configuration details, including keyword texts, match types, bid amounts, and performance metrics such as spend, impressions, and clicks for Walmart Connect Ads.

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