How to Use the Weiban Assistant MCP in AutoGen

Q: What kind of input does the MCP Server support for AutoGen?

The server supports calls to many tools—from createlead to getgroupchat. You pass these functions, and the agents learn how to decide which tool is needed at any step of their debate.

Force consensus on tasks involving Weiban Assistant using AutoGen.

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Connect Weiban Assistant MCP to AutoGen

Create your Vinkius account to connect Weiban Assistant 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 Weiban Assistant with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate Sales Strategy with the MCP Server

You can set up two agents: a 'Risk Agent' and an 'Opportunity Agent.' The Risk agent reviews `list_leads` for compliance issues, while the Opportunity agent uses `get_customer_details` to find immediate buying signals. They debate which leads should be prioritized based on competing criteria.

Analyze Staff Performance with Weiban Assistant and AutoGen

Design an argument where one agent reviews `list_staff` for roles, and a second agent calls `get_staff_stats`. They negotiate the final recommendation: Is the observed staff behavior (the stats) actually correlated to high performance? This forces a deep dive into internal metrics.

Resolve Group Chat Conflicts using MCP Server

Have agents debate group chat outcomes. One agent calls `list_group_chats` and another uses `get_group_chat`. They then argue over the best action to take, based on whether the conversation was productive or stalled.

Setup guide

Set up Weiban Assistant 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 Weiban Assistant 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="Weiban Assistant_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Weiban Assistant 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 Weiban Assistant. 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 Weiban Assistant MCP in AutoGen

The system uses deliberation. Agents don't just execute; they challenge each other's findings using the tools like `list_leads` or `get_org_summary`, converging on a decision only after debate.

Yes. It manages conversational data by having agents discuss chat histories (`list_chat_records`) and organizational summaries, simulating real-world team consensus.

The server supports calls to many tools—from `create_lead` to `get_group_chat`. You pass these functions, and the agents learn how to decide which tool is needed at any step of their debate.

It's built for persistence. Since it’s a multi-agent system, the conversational history *is* the persistent context—the agents remember what they debated in previous turns.

This server touches communication-messaging data. It involves chat records, customer details, and organizational activity summaries that the agents debate over.

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