How to Use the Qualified.io MCP in AutoGen

Q: How do I register the Qualified.io MCP Server tools with AutoGen agents?

Use mcpservertools from autogen-ext to load the tools. You can then pass the tools list directly to your AssistantAgent constructor so they can run commands like listassessments.

Q: How do AutoGen agents write candidate reviews on Qualified.io?

An agent pulls the test details using getassessmentresult, analyzes the code, and shares it with the team. Once the agents agree on a score, the writer agent calls createassessmentresultreview to save it.

Q: What happens if a candidate needs to retake a test in an AutoGen workflow?

A monitoring agent can flag failed submissions from listassessmentresults and ask the coordinator agent for approval. If approved, the coordinator agent executes scheduleretryassessmentresult to reopen the test.

Q: How is candidate code privacy handled during AutoGen multi-agent reviews?

Code submissions pulled via getassessmentresultexhibit are processed strictly in transit through Vinkius's zero-trust gateway. Your candidate data is never logged or exposed to external networks during the agent debate.

Run multi-agent debates in AutoGen to evaluate Qualified.io test results and reach consensus on candidate hires.

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Setup Qualified.io with AutoGen

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Collaborative candidate evaluation debates

The Qualified.io MCP Server enables your AutoGen agents to collaborate on candidate reviews by pulling test data with `get_assessment_result`. A reviewer agent analyzes the code quality, while a recruiter agent checks the completion speed. The agents debate the candidate's performance in a group chat before deciding whether to pass them. Once they reach a consensus, the lead agent calls `create_assessment_result_review` to post the final decision.

Automated test lifecycle coordination

This MCP toolset lets your AutoGen agents manage the active test library through structured conversation. One agent can request a test update, prompting another to call `list_assessments` and identify outdated tests. The coordinator agent then executes `unpublish_assessment` and `archive_assessment` based on the group's decision. This multi-agent coordination ensures that no assessment is modified without proper validation.

Multi-agent candidate invitation workflows

Your AutoGen team can manage candidate outreach by coordinating invite tasks. A sourcing agent identifies candidates, while the coordinator agent calls `invite_candidates_via_cohort` to send the tests. If a candidate declines or needs to be removed, a separate agent can call `cancel_invitation` to clean up the queue. This keeps your candidate pipeline organized without manual recruiter intervention.

Setup guide

Set up Qualified.io 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 Qualified.io 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="Qualified.io_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Qualified.io. 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 Qualified.io MCP in AutoGen

Use `mcp_server_tools` from `autogen-ext` to load the tools. You can then pass the tools list directly to your `AssistantAgent` constructor so they can run commands like `list_assessments`.

Yes, one agent can review a candidate's resume while another checks active assessments with `list_assessments`. They discuss suitability and, if they agree, one agent triggers `invite_candidates` to send the test.

An agent pulls the test details using `get_assessment_result`, analyzes the code, and shares it with the team. Once the agents agree on a score, the writer agent calls `create_assessment_result_review` to save it.

A monitoring agent can flag failed submissions from `list_assessment_results` and ask the coordinator agent for approval. If approved, the coordinator agent executes `schedule_retry_assessment_result` to reopen the test.

Code submissions pulled via `get_assessment_result_exhibit` are processed strictly in transit through Vinkius's zero-trust gateway. Your candidate data is never logged or exposed to external networks during the agent debate.

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