How to Use the LinkedIn Ads MCP in AutoGen

Q: How do I register LinkedIn Ads MCP tools with an AutoGen agent?

Use mcpservertools with your server URL to fetch the tool definitions, then pass them to your AssistantAgent constructor. The adapter handles the schema conversion automatically.

Q: How do AutoGen agents handle conflicting LinkedIn Ads data?

They debate it. If getadanalytics shows high clicks but listconversionrules shows zero conversions, your analyst agent will flag the discrepancy to your strategy agent for investigation.

Let your AutoGen agents debate and optimize your LinkedIn Ads performance using this MCP Server.

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Connect LinkedIn Ads MCP to AutoGen

Create your Vinkius account to connect LinkedIn 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 LinkedIn Ads with AutoGen

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Multi-agent debate over LinkedIn Ads performance in AutoGen

Set up an AutoGen debate where a performance agent queries `get_ad_analytics` via this MCP connection while a finance agent checks budget constraints. They argue over bid adjustments using real data. The performance agent uses `list_ad_campaigns` to find high-cost items, while the finance agent verifies against internal targets to reach a consensus on which budgets to slash.

Consensus-driven creative audits

Let one AutoGen agent pull active assets using `list_ad_creatives` while a copywriter agent evaluates the messaging. They collaborate to draft better variations based on historical performance. They verify their conclusions by checking `list_conversion_rules` to ensure the new copy aligns perfectly with the tracked conversion events on your landing pages.

Autonomous account restructuring

Deploy a group chat of AutoGen agents to audit your account setup. They use `list_ad_accounts` and `list_campaign_groups` to map out messy, overlapping target audiences. This multi-agent team identifies duplicate targets and recommends structural cleanups directly inside your AutoGen workflow manager.

Setup guide

Set up LinkedIn 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 LinkedIn 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="LinkedIn Ads_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent LinkedIn 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 LinkedIn 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 LinkedIn Ads MCP in AutoGen

Use mcp_server_tools with your server URL to fetch the tool definitions, then pass them to your AssistantAgent constructor. The adapter handles the schema conversion automatically.

Yes. You can design a group chat where one agent pulls metrics via `get_ad_analytics` and another agent analyzes the performance of specific campaign groups before making a decision.

They debate it. If `get_ad_analytics` shows high clicks but `list_conversion_rules` shows zero conversions, your analyst agent will flag the discrepancy to your strategy agent for investigation.

Yes, the server runs on a fast, asynchronous V8 sandbox on Vinkius, ensuring your AutoGen agent conversations aren't delayed by slow API response parsing.

No. The Vinkius sandbox isolates all tool executions. Your AutoGen agents only receive the specific JSON payloads returned by `list_ad_accounts` or other tools, keeping your credentials secure.

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