How to Use the WorkAdventure MCP in AutoGen

Q: Can I use AutoGen to check multiple map details for WorkAdventure?

Yes. You can assign an agent the task of calling listmaps, and then have it iterate through those results, making a call to getmapdetails for each one until consensus is reached.

Q: Does AutoGen handle data privacy when using WorkAdventure? (FAQ 5)

The server touches member details, which are used during the debate process. The agents only access and process the specific member information required to reach a decision.

Q: What's the best workflow for AutoGen and room variables?

You let one agent check current variables with getroomvariables, and another proposes an update using updateroomvariable. The debate confirms if that change is safe and necessary.

Q: How do I list members for a group consensus in WorkAdventure?

One agent calls listmembers to gather the initial population data. This list then becomes the input dataset for all subsequent agents debating membership status or roles.

Engineer consensus decisions for WorkAdventure using AutoGen's multi-agent debate.

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Setup WorkAdventure with AutoGen

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Perplexity

Debate optimal virtual space setup.

Set up competing agents. One agent might use `get_world_info` to determine current capacity, while another uses `list_members` to track density. They debate whether the current world is too crowded and decide if a new map needs creation. The final consensus isn't just data; it's an agreement reached through structured conflict between defined roles.

Negotiate changes to room variables.

You can deploy agents with opposing goals. A 'Security Agent' checks `get_room_variables` for compliance status, and a 'Performance Agent' uses `update_room_variable` to implement the necessary fix. The negotiation leads to a validated state change. This allows you to model complex operational workflows where decisions require multiple perspectives.

Manage member lifecycle via debate.

A group of agents can decide on a member’s status. One agent might check `get_member_details` for an account expiration, and another uses that information to trigger the `delete_member` tool call after confirming all necessary handoffs are complete. The system simulates human consensus before taking destructive or critical actions.

Setup guide

Set up WorkAdventure 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 WorkAdventure 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="WorkAdventure_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You give different agents tools. The agents then debate, using those tools in sequence, to achieve a final agreed-upon outcome or decision point.

Yes. You can assign an agent the task of calling `list_maps`, and then have it iterate through those results, making a call to `get_map_details` for each one until consensus is reached.

The server touches `member details`, which are used during the debate process. The agents only access and process the specific member information required to reach a decision.

You let one agent check current variables with `get_room_variables`, and another proposes an update using `update_room_variable`. The debate confirms if that change is safe and necessary.

One agent calls `list_members` to gather the initial population data. This list then becomes the input dataset for all subsequent agents debating membership status or roles.

Use it with your favorite AI tools

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