How to Use the Liftoff MCP in AutoGen
Deploy AutoGen agents to debate and optimize your Liftoff mobile ad spend.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Liftoff MCP to AutoGen
Create your Vinkius account to connect Liftoff 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.
Debate ad performance using AutoGen and Liftoff
The `get_spend_metrics` tool feeds synchronous spend data into your AutoGen multi-agent system, allowing different agents to analyze the numbers from competing angles. In your AutoGen group chat, a performance agent might push to scale Liftoff budget based on low cost-per-install, while a finance agent challenges the ROI. These AutoGen agents debate the metrics in a structured conversation loop, ensuring you don't make rash Liftoff budget changes. They use the real-time Liftoff spend data to reach a consensus before recommending any AutoGen action.
Coordinate multi-agent Liftoff reporting pipelines
The `request_performance_report` tool is triggered by a coordinator agent in AutoGen, which then delegates status monitoring to a dedicated polling agent. This polling agent calls `get_report_status` at set intervals without blocking the AutoGen coordinator from executing other Liftoff tasks. Once the Liftoff report is ready, the polling agent alerts an AutoGen data-analyst agent to run `download_report_results` and parse the CSV. This AutoGen multi-agent division of labor makes handling asynchronous Liftoff reports highly efficient.
Audit creative asset distribution with an MCP Server agent
The `list_liftoff_creatives` tool allows an asset-auditing agent in AutoGen to inspect your creative inventory. This agent compares the active Liftoff creatives against the app list retrieved by `list_liftoff_apps` to find apps missing fresh visual assets in AutoGen. The auditing agent then drafts a summary of missing Liftoff creatives and presents it to your AutoGen design coordinator agent. Using this MCP Server setup, your AutoGen agents handle Liftoff inventory checks autonomously, flagging gaps in your ad coverage.
Set up Liftoff 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes Liftoff tools and returns structured results.
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="Liftoff_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Liftoff data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="Liftoff_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Liftoff data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Liftoff. 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 Liftoff MCP in AutoGen
Use it with your favorite AI tools
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