How to Use the WorkWave MCP in AutoGen

Q: Can AutoGen use WorkWave to find service locations?

Yes. It uses listservicelocations as a starting point and can then cross-reference those job sites with specific customer accounts via other tools in the debate.

Resolve complex WorkWave decisions by having multiple AI agents debate the best course of action using AutoGen.

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Connect WorkWave MCP to AutoGen

Create your Vinkius account to connect WorkWave 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 WorkWave with AutoGen

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Perplexity

Determine optimal order status updates.

Need to know if an order should be Completed? You can set up a multi-agent discussion. One agent calls `get_order_details` and suggests 'Complete.' A second, risk-checking agent might challenge this, demanding verification from `list_service_equipment`. The agents debate until consensus is reached. The user doesn't just execute; they facilitate deliberation between competing technical viewpoints.

Validate resource allocation plans.

Imagine a scenario where an agent needs to assign staff. Agent A calls `list_field_employees` and suggests John Doe. Agent B, the auditor, then checks if John is assigned to that area by calling `list_service_locations`. The agents negotiate who is actually available at the site. This system resolves conflict over resource availability through structured discussion.

Verify customer eligibility and assets.

When onboarding a new service, one agent gets basic info via `get_account_details`. Another agent calls `list_service_accounts` to confirm the account exists. A third then checks what equipment is needed by calling `list_service_equipment`. The agents work together to ensure all necessary data points are cross-validated before any action is taken.

Setup guide

Set up WorkWave 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 WorkWave 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="WorkWave_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You can set up a debate between agents. One agent might try to update status, while another flags that the required equipment is missing. The negotiation process forces a more accurate decision.

Yes. It uses `list_service_locations` as a starting point and can then cross-reference those job sites with specific customer accounts via other tools in the debate.

The server provides service order details, account information, employee profiles, and equipment lists. These structured datasets form the basis of agent deliberation.

Instead of just checking the list, you can have agents debate the *reason* for a specific order status. One might pull `get_order_details` while another confirms if related assets are listed in `list_service_equipment`.

Because the server touches customer accounts and order status, agents must be carefully instructed. You need to ensure that only necessary data points are discussed or acted upon during deliberation.

Use it with your favorite AI tools

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