How to Use the AdButler MCP in AutoGen

Q: How do I register the AdButler MCP tools with an AutoGen agent?

Use mcpservertools with your Vinkius HTTP URL to load the tools. Pass the resulting list directly into your AssistantAgent constructor.

Build AutoGen multi-agent teams that use this MCP Server to debate and optimize AdButler campaigns.

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Connect AdButler MCP to AutoGen

Create your Vinkius account to connect AdButler 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 AdButler with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Consensus-driven campaign optimization

`get_performance_report` provides the raw data for your agent debates. One agent analyzes low CTRs, while another flags high-performing zones, forcing them to negotiate the best optimization strategy. Instead of a single agent making blind changes, AutoGen coordinates a discussion. The agents reach a consensus on which zones need adjustment based on real-time metrics.

Creative compliance audits via AutoGen

`list_creatives` pulls your asset library into the agent conversation via the MCP interface. A compliance agent checks banner specs while a marketing agent reviews the creative copy, ensuring everything matches campaign guidelines. This collaborative review catches errors before campaigns go live. The agents cross-reference the active assets against your publisher requirements automatically.

Automated publisher and zone mapping

`list_publishers` and `list_zones` allow your agent team to audit your ad network structure. One agent lists the active publishers, while another queries specific zones to find orphan placements. The agents work together to compile a clean report of your inventory. This ensures your campaigns are only assigned to active, verified publisher zones.

Setup guide

Set up AdButler 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 AdButler 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="AdButler_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent AdButler 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="AdButler_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent AdButler 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 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 AutoGen

Use mcp_server_tools with your Vinkius HTTP URL to load the tools. Pass the resulting list directly into your AssistantAgent constructor.

Yes. Once registered, any agent in your conversational workflow can call list_campaigns to check pending items or verify status.

The agents use conversational consensus. One agent might pull metrics using get_performance_report and propose a change, while another challenges the decision based on publisher constraints.

The adapter handles both stdio and Streamable HTTP transports, making it easy to run your agents locally or in a cloud environment.

Vinkius runs the server in an isolated V8 sandbox. Your creative files, publisher lists, and raw ad performance data pass directly to your AutoGen client without being stored on our servers.

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