How to Use the Unanet MCP in AutoGen

Q: Does this MCP Server support AutoGen for complex expense analysis?

It does. Agents can review expenses and challenge the data—for instance, one agent might flag missing receipts while another calculates total cost against a project.

Q: What is required to get started with Unanet and AutoGen together?

You pass the list of tools (like projects) into the AssistantAgent constructor. The adapter handles converting the tool schema automatically for the conversational flow.

Q: What kind of data does this MCP Server touch regarding employee details?

The server uses the users tool to access basic personnel records, which agents can then debate and cross-reference with other activity logs like timesheets.

Drive consensus decisions across Unanet data using AutoGen agents.

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Connect Unanet MCP to AutoGen

Create your Vinkius account to connect Unanet 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 Unanet with AutoGen

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Perplexity

AutoGen: Debate project status with the MCP Server

You can set up a group of agents to debate the viability of a project. One agent might check `projects` for deadlines, while another checks `users` to see resource availability. The process is consensus-driven; the system requires multiple perspectives—a risk assessment from one agent and a feasibility report from another.

Reviewing payroll data with `expenses`

Use agents to review expense reports. One agent can check if an expense exceeds department budgets, while another validates the associated user against current employee records (`users`). This forces a deliberation process until all parties agree on the status of the spend.

Analyzing time allocation with `timesheets`

You can deploy agents to analyze timesheet data. One agent might summarize total hours logged, and another might challenge those totals by cross-referencing them against project scope limits defined in the system. This negotiation capability is perfect for figuring out resource bottlenecks.

Setup guide

Set up Unanet 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 Unanet 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="Unanet_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Unanet data")
    print(result.messages[-1].content)

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Common questions about Unanet MCP in AutoGen

AutoGen doesn't just return a list; it forces agents to debate the findings. If `users` and `projects` provide conflicting information, the system requires consensus before presenting an answer.

Yes. You build systems where the final conclusion isn't obvious, requiring deliberation. For example, debating whether to approve a new project based on current resource usage.

It does. Agents can review `expenses` and challenge the data—for instance, one agent might flag missing receipts while another calculates total cost against a project.

You pass the list of tools (like `projects`) into the AssistantAgent constructor. The adapter handles converting the tool schema automatically for the conversational flow.

The server uses the `users` tool to access basic personnel records, which agents can then debate and cross-reference with other activity logs like timesheets.

Use it with your favorite AI tools

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