How to Use the GeTui / 个推 MCP in AutoGen

Q: How do I configure GeTui / 个推 tools inside AutoGen?

Run pip install -U autogen-extmcp. Initialize the MCP tools with the endpoint URL. AutoGen handles the schema conversion automatically when you pass the tools to your AssistantAgent.

Build multi-agent debate systems for GeTui broadcasts in AutoGen using this MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect GeTui / 个推 MCP to AutoGen

Create your Vinkius account to connect GeTui / 个推 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.

GDPR Free for Subscribers

Setup GeTui / 个推 with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Consensus-driven message delivery

`push_to_all` and `push_to_list` require agreement between your AutoGen agents before execution. A marketing agent drafts the notification payload, while a compliance agent reviews the copy and timing. They debate the campaign parameters in a shared chat. Only when both agents reach a consensus does the system actually fire the command to the GeTui API. This prevents accidental spam and enforces strict quality control.

Collaborative audience mapping via MCP Server

`bind_user_alias` and `query_user_alias` let your agents manage device identities together. One agent handles the database lookups while another formats the mapping requests for the MCP integration. If a user changes devices, the agents detect the stale CID. They run `get_cid_status` to confirm the device is offline, then update the alias mapping automatically.

Multi-perspective campaign analysis

`get_daily_push_report` and `get_app_user_stats` feed raw numbers into your agent conversation. An analytics agent interprets the open rates, while a technical agent looks at the API failure logs. They argue about why a specific push failed. The technical agent might run `get_push_status` to pull individual task records to prove a network timeout, forcing the marketing agent to adjust the next send window.

Setup guide

Set up GeTui / 个推 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 GeTui / 个推 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="GeTui / 个推_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent GeTui / 个推 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="GeTui / 个推_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent GeTui / 个推 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 GeTui / 个推. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect GeTui / 个推 now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about GeTui / 个推 MCP in AutoGen

Run `pip install -U autogen-ext[mcp]`. Initialize the MCP tools with the endpoint URL. AutoGen handles the schema conversion automatically when you pass the tools to your `AssistantAgent`.

Yes. You inject the tools into the shared environment. Any agent in the conversation can call a tool if the group decides it needs more data.

Require human-in-the-loop approval for the push commands. Let the agents run the reporting and status checks autonomously, but force a manual confirm before they broadcast.

It uses Streamable HTTP transports to handle the connections. The tool execution results stream directly back into the agent chat for immediate evaluation.

The V8 Isolate sandbox destroys its memory space after every execution. When your agents debate user tags or CID statuses, that sensitive telemetry never touches persistent storage on our end.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript