How to Use the WebHR MCP in AutoGen

Facilitate consensus decisions on WebHR processes with AutoGen's multi-agent debate framework.

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Connect WebHR MCP to AutoGen

Create your Vinkius account to connect WebHR 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 WebHR with AutoGen

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Debate HR Policy Decisions using WebHR MCP Server

The best way to update policy is through argument. Set up a system where an 'Audit Agent' uses `list_attendance_logs` and challenges the 'Compliance Agent,' which checks rules via `list_available_leave_types`. They debate until they converge on a final, agreed-upon leave rule. AutoGen lets you build systems that require deliberation. It's perfect when the answer isn't obvious and needs input from competing perspectives talking to each other.

Simulate Hiring Consensus with AutoGen

Hiring decisions are complex. Give an 'HR Manager Agent' access to `list_job_postings` and let it debate the scope of work with a 'Department Head Agent,' which references `list_company_departments`. They negotiate the job requirements until they reach consensus on the final requisition. This is about negotiation, not execution. The answer comes from multiple agents discussing and challenging each other's initial assumptions.

Structure Employee Data using AutoGen

To process a new employee transfer, you don't just run one command. You set up an 'HR Agent' to pull `get_employee_details`, which then passes the data to a 'System Agent' that verifies office location rules via `list_office_locations`. They pass back and forth until the profile is fully validated. AutoGen handles this flow naturally. It allows multiple agents to collaborate, ensuring all required steps—like checking job requests (`list_job_requests`) and employee records—are completed before proceeding.

Setup guide

Set up WebHR 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 WebHR 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="WebHR_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen doesn't just execute tools; it makes agents debate the results. If one agent calls `list_employees`, a second agent can critique that list based on department rules, leading to a more robust outcome.

Any decision requiring consensus. For instance, figuring out if an employee's leave request is valid requires comparing `list_leave_requests` against department policies—a perfect debate scenario.

Yes. You can set up agents to review the open jobs listed by `list_job_postings` and compare them against internal needs tracked in `list_job_requests`, forcing a negotiation on resource allocation.

It's extremely high. Because the outcome requires deliberation between competing agent perspectives, the final decision is generally more thorough and less likely to miss edge cases.

The most sensitive data this server touches are personal employee records, specifically details like names and current roles from `get_employee_details` and attendance logs. Agents must be constrained to use the endpoint token.

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