How to Use the Workvivo MCP in AutoGen

Q: How do I use Workvivo with AutoGen to list posts?

Agents first call listworkvivoposts to gather the feed data. They then debate which specific post IDs are most relevant, and finally pass those confirmed IDs to another tool for detail retrieval. The deliberation is key here.

Q: If I use Workvivo and AutoGen, how do I check spaces?

One agent calls listcollaborationspaces to see all available groups. A second agent then takes that list and uses getspacedetails on a selected ID. The agents argue until they've successfully gathered all the required context.

Drive consensus decisions with AutoGen: Multi-agent workflows for Workvivo management.

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Create your Vinkius account to connect Workvivo 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 Workvivo with AutoGen

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A security agent might first use `list_employees` to check credentials, while a performance agent simultaneously calls `get_employee_profile` for necessary details. AutoGen makes these agents debate and agree on which data is needed before proceeding. This process forces consensus among the agents, ensuring that every action—like creating a post via `create_social_post`—is validated by multiple perspectives.

Structure multi-step Workvivo content creation

An agent team can debate whether to create a new post. One agent might list available spaces using `list_collaboration_spaces`, and another might retrieve event details via `get_event_details`. The final, agreed-upon decision then calls `create_social_post` with the right context. It’s not just executing steps; it's negotiating *how* those steps should run.

Manage Workvivo content through deliberation

If you need to remove a post, the agents don't just delete. They discuss it: one agent confirms ownership via `get_post_details`, and another flags the action as irreversible using `delete_social_post`. The negotiation ensures the user is aware of the consequence. This robust debate system handles critical actions by requiring multiple points of agreement.

Setup guide

Set up Workvivo 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 Workvivo 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="Workvivo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Agents first call `list_workvivo_posts` to gather the feed data. They then debate which specific post IDs are most relevant, and finally pass those confirmed IDs to another tool for detail retrieval. The deliberation is key here.

Absolutely. You can set up a multi-agent conversation where agents debate the best time/place. They might use `list_company_events` to gather options, which then leads to calling `get_event_details` for the final recommendation.

The server touches employee profile details and social posts. Because of the multi-agent debate, every piece of information—including specific post content or profile attributes—is vetted by multiple virtual agents before final action.

Yes. The entire framework is built for it. You build systems where the answer isn't obvious and requires competing perspectives to argue toward a single, agreed-upon conclusion.

One agent calls `list_collaboration_spaces` to see all available groups. A second agent then takes that list and uses `get_space_details` on a selected ID. The agents argue until they've successfully gathered all the required context.

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