How to Use the Factored Quality MCP in AutoGen

Q: Can AutoGen agents disagree on Factored Quality reports?

Yes. If one agent finds a critical failure in getlabtest data, it can flag the issue. The other agent must then address this concern before the task continues.

Debate your supply chain risks in AutoGen by having specialized agents negotiate using live Factored Quality inspection data.

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Connect Factored Quality MCP to AutoGen

Create your Vinkius account to connect Factored Quality 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 Factored Quality with AutoGen

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Negotiate risks with AutoGen agents

Set up a security agent to check `get_inspection` results while a manager agent reviews the costs. They debate the findings to reach a consensus on whether to proceed with a booking. This deliberation process catches issues that a single agent might miss. The agents challenge each other based on the data retrieved from the server.

Automate QC bookings through consensus

Assign an agent to call `create_booking` only after the team agrees on the details. This prevents accidental service requests based on incomplete information. The agents verify the supplier details using `get_supplier` before finalizing the booking. You get a documented decision trail for every action taken.

Handle tool schemas with McpToolAdapter

The adapter automatically converts the Factored Quality tool definitions for your AutoGen agents. You don't need to manually map the schemas for the agents to understand the API. This setup works across different transport types, including HTTP. You pass the tool list directly to the agent constructor and start the conversation.

Setup guide

Set up Factored Quality 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 Factored Quality 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="Factored Quality_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Agents pass tool output back and forth in the conversation history. This allows them to reference the same inspection records during their deliberation process.

Yes. If one agent finds a critical failure in `get_lab_test` data, it can flag the issue. The other agent must then address this concern before the task continues.

The MCP server enforces access controls on every tool execution. Your AutoGen agents only receive the specific data requested, ensuring audit logs remain secure.

The agents decide which tool to call based on the current context. If an inspection is flagged, the agent might automatically call `get_audit` to investigate further.

The server supports streamable HTTP transports. This allows your agents to receive large audit reports in chunks, keeping the conversation responsive during long lookups.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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