How to Use the Mobile Action MCP in AutoGen
Deploy AutoGen agents to debate keyword strategies, audit ad creatives, and build consensus on app store optimization.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Mobile Action MCP to AutoGen
Create your Vinkius account to connect Mobile Action 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.
Run multi-agent ASO debates with an MCP Server
The Mobile Action MCP Server enables collaborative decision-making among specialized agents. An ASO strategist agent calls `get_tracked_keywords` to analyze current performance, while a media buyer agent runs `get_ad_creatives` to assess paid campaigns. These agents debate the optimal allocation of your marketing budget. Look, they review actual download data and converge on a balanced acquisition strategy without human intervention.
Audit custom product pages via collaborative agents
Your agent workflow uses `get_cpp_details` to retrieve active custom product page layouts. A creative director agent reviews the visual assets, while a copywriter agent evaluates the messaging based on `get_app_info` data. The agents negotiate changes to improve store conversion rates. They cross-reference their ideas with competitor benchmarks pulled from `get_tracked_apps` to ensure your layouts stand out.
Forecast market trends with consensus-driven agents
One agent calls `get_market_estimations` to track revenue and download trends for your category. A second agent queries `get_related_keywords` to identify search terms that are gaining traction. They compare notes to forecast which keywords will drive the most profitable traffic. This cooperative analysis helps you adjust your metadata before competitors react to the same market shifts.
Set up Mobile Action 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 Mobile Action 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="Mobile Action_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Mobile Action 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="Mobile Action_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Mobile Action 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 Mobile Action. 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 Mobile Action MCP in AutoGen
Use it with your favorite AI tools
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