How to Use the Zephyr Scale (SmartBear) MCP in AutoGen

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Connect Zephyr Scale (SmartBear) MCP to AutoGen

Create your Vinkius account to connect Zephyr Scale (SmartBear) 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 Zephyr Scale (SmartBear) with AutoGen

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MCP Server: Debating Test Statuses with AutoGen

When multiple stakeholders need to agree on the project status, let your agents debate it. One agent calls `list_test_cases` for all keys, while another retrieves individual run details using `get_test_case`. They then argue over which combined state is accurate. This uses AutoGen's core strength: consensus-driven decision making. The answer isn't in one API call; it requires multiple agents to challenge each other's findings.

Automating Test Cycle Definition with AutoGen

Defining a release plan needs negotiation. Have Agent A list all existing test cycles (`list_test_cycles`), and Agent B fetch the full details of potential plans using `get_test_cycle`. They debate which combination represents the best scope for the sprint. AutoGen lets you build systems where agreement is reached through structured deliberation between competing viewpoints.

MCP Server: Determining Project Scope with AutoGen

To define a project's boundaries, have one agent list all available test plans (`list_test_plans`), and another check the current environments via `list_environments`. They negotiate which combination of scope and deployment target is valid. This makes complex planning decisions. The agents don't just execute tools; they discuss what those tool outputs mean for the overall project state.

Setup guide

Set up Zephyr Scale (SmartBear) 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 Zephyr Scale (SmartBear) 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="Zephyr Scale (SmartBear)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zephyr Scale (SmartBear) 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="Zephyr Scale (SmartBear)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zephyr Scale (SmartBear) 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 Zephyr Scale (SmartBear). 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 Zephyr Scale (SmartBear) MCP in AutoGen

AutoGen treats the MCP Server as a source of objective data. Instead of one agent running all calls, multiple agents use different tools to gather conflicting or complementary views on test requirements.

Yes. You can set up a multi-agent system where one agent checks `list_test_cases` while another checks `list_statuses`, forcing the agents to reconcile all possible states.

The benefit is consensus. You get a final, debated decision on test requirements that has been reviewed by multiple specialized AI agents, rather than just the output of a single tool.

It does. The framework handles passing the schema-defined outputs—like all test case keys from `list_test_cases`—between specialized agents for discussion.

This server touches test execution statuses. You can list all custom test execution statuses using `list_statuses`, which is critical input for agent deliberation.

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