How to Use the Zendesk QA (Klaus) MCP in AutoGen

Q: How do I get all necessary review data for debate with Zendesk QA (Klaus) and AutoGen?

Start by calling listallreviews to gather everything. Then, you can pass that full dataset into the AutoGen conversation framework for multiple agents to discuss.

Q: Can I feed new ticket data into my multi-agent system with Zendesk QA (Klaus) and AutoGen?

Yes. Use importqatickets as an initial step before the conversation begins. This ensures that all agents debate using the most up-to-date support transcripts.

Q: What is the best way to scope agent discussion with Zendesk QA (Klaus) and AutoGen?

Use listqaworkspaces first. This identifies the boundaries, allowing agents to focus their debate only on reviews from that specific workspace.

Q: If I need to verify a specific user's role for my agents with Zendesk QA (Klaus) and AutoGen?

You can use importqausers to sync agent and manager records. This provides the system with validated roles required for informed decision-making.

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Connect Zendesk QA (Klaus) MCP to AutoGen

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Setup Zendesk QA (Klaus) with AutoGen

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Debate across all quality assurance reviews.

The `list_all_reviews` tool feeds data into the conversation, allowing different agents to challenge review findings. You can scope this debate using `list_workspace_reviews`, focusing the discussion on a single area. This consensus-driven approach helps your system decide not just what happened, but why it mattered.

Synchronize and prepare input data for agents.

Before debate starts, you need clean inputs. Run `import_qa_tickets` to sync conversation history or use `import_qa_users` to ensure all agent profiles are current. These structured inputs allow the autonomous agents to discuss concrete facts rather than making assumptions.

Pinpoint issues by searching conversations.

The `search_qa_conversations` tool gives the system a precise starting point. The multiple agents can then work together to narrow down vague questions using these specific search results. This accelerates decision-making; one agent suggests a search, and another validates the scope.

Setup guide

Set up Zendesk QA (Klaus) 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 Zendesk QA (Klaus) 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="Zendesk QA (Klaus)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Zendesk QA (Klaus)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zendesk QA (Klaus) 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 Zendesk QA (Klaus). 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 Zendesk QA (Klaus) MCP in AutoGen

Start by calling `list_all_reviews` to gather everything. Then, you can pass that full dataset into the AutoGen conversation framework for multiple agents to discuss.

Yes. Use `import_qa_tickets` as an initial step before the conversation begins. This ensures that all agents debate using the most up-to-date support transcripts.

Use `list_qa_workspaces` first. This identifies the boundaries, allowing agents to focus their debate only on reviews from that specific workspace.

It handles conversation transcripts, review records, and agent/user profiles. These discrete data types provide the necessary facts for multi-agent debate.

You can use `import_qa_users` to sync agent and manager records. This provides the system with validated roles required for informed decision-making.

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