How to Use the Jiguang Aurora / 极光 MCP in AutoGen

Q: How do I configure Jiguang Aurora / 极光 inside AutoGen?

Install the extension with pip install -U autogen-extmcp and use the streamable HTTP parameters. This MCP Server integration passes the tools to your assistant agent constructor.

Q: Can AutoGen agents collaborate to schedule a Jiguang Aurora / 极光 push?

Yes. One agent checks the quota using getaccountquota and another calls createschedule once the first agent confirms there is enough room.

Q: How do AutoGen agents handle failed Jiguang Aurora / 极光 deliveries?

An agent calls getmessagestatus to find out why a push failed. It shares this error with the supervisor agent, which then decides whether to retry or update the device tag.

Coordinate multi-agent debates in AutoGen to validate, schedule, and send Jiguang Aurora / 极光 push notifications.

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Create your Vinkius account to connect Jiguang Aurora / 极光 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 Jiguang Aurora / 极光 with AutoGen

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Let agents debate your Jiguang Aurora / 极光 campaigns

Avoid bad marketing decisions. In AutoGen, your marketing agent drafts a notification and calls `send_push`. Before it goes out, your compliance agent steps in to inspect the payload and target audience. They debate the timing and the target list. If the compliance agent flags an issue, the task is updated or cancelled before a single byte reaches the Jiguang servers.

Manage schedules through consensus-driven agents

Scheduling notifications requires balance. One agent uses `create_schedule` on the MCP Server to set up a campaign, while another agent monitors system limits with `get_account_quota`. They negotiate the best time slot based on active queues. The agents inspect the active queue using `list_schedules`. If a conflict arises, they resolve it together and call `delete_schedule` on the lower-priority task without human intervention.

Clean device targets using a multi-agent MCP Server loop

Keep your targeting clean through automated collaboration. A data agent pulls device details with `get_device_info` while an analyst agent evaluates engagement using `get_user_report`. They decide which users are stale. Once they agree, the data agent calls `update_device` to remove tags from inactive devices, keeping your audience segments highly targeted.

Setup guide

Set up Jiguang Aurora / 极光 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 Jiguang Aurora / 极光 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="Jiguang Aurora / 极光_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Jiguang Aurora / 极光 data")
print(result.messages[-1].content)

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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="Jiguang Aurora / 极光_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Jiguang Aurora / 极光 data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Jiguang Aurora / 极光. 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 Jiguang Aurora / 极光 MCP in AutoGen

Install the extension with pip install -U autogen-ext[mcp] and use the streamable HTTP parameters. This MCP Server integration passes the tools to your assistant agent constructor.

Yes. One agent checks the quota using get_account_quota and another calls create_schedule once the first agent confirms there is enough room.

An agent calls get_message_status to find out why a push failed. It shares this error with the supervisor agent, which then decides whether to retry or update the device tag.

Yes, the tool adapter supports both stdio and streamable HTTP transports. This makes it easy to host the server remotely and connect your agents.

It accesses push payloads, device tags, and delivery reports. All API credentials are isolated in a secure sandbox, ensuring your master keys never touch the agent conversation logs.

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