How to Use the Zephyr Scale (SmartBear) MCP in Pydantic AI

Q: What is the best way to check test coverage?

Start by calling listtestcases. This gives you a structured list of every available test case key and name, letting your agent build its scope correctly.

Q: What's the difference between gettestcase and other retrieval tools?

gettestcase pulls all details for one single case. Other tools, like listexecutions, give you aggregated lists of results or statuses.

Guaranteed structure for test data with Pydantic AI. Manage Zephyr Scale (SmartBear) tests without validation errors.

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Connect Zephyr Scale (SmartBear) MCP to Pydantic AI

Create your Vinkius account to connect Zephyr Scale (SmartBear) to Pydantic AI 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 Pydantic AI

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Find all available test cases reliably

The `list_test_cases` tool fetches every test case key, name, and status in a project. Since the output is Pydantic-validated, you don't have to worry about hallucinated or missing fields. This guarantees your agent only gets correctly formatted data types.

Verify deep test execution details

Use `get_execution` to pull a complete record of one test run. The response is validated against Pydantic models, so if the API returns junk, your agent fails loud and clear. It's correct, or it breaks—no ambiguity.

Locate specific project artifacts

If you only need to check a folder type, `list_folders` handles that. You can also get the full details of one case using `get_test_case`. All these endpoints return guaranteed schema-compliant data. This makes building reliable, production-grade agents simple.

Setup guide

Set up Zephyr Scale (SmartBear) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zephyr-scale-smartbear-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Zephyr Scale (SmartBear) tools.",
)

result = await agent.run("List recent Zephyr Scale (SmartBear) transactions")
print(result.output)

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Common questions about Zephyr Scale (SmartBear) MCP in Pydantic AI

It validates every MCP response against predefined Pydantic models at runtime. If the API sends unexpected JSON, your agent throws a validation error instead of silently processing bad data.

Start by calling `list_test_cases`. This gives you a structured list of every available test case key and name, letting your agent build its scope correctly.

Yes. The framework supports modeling multiple return types—test plans, cycles, or executions—ensuring that every piece of retrieved data fits a strict, defined structure.

`get_test_case` pulls all details for one single case. Other tools, like `list_executions`, give you aggregated lists of results or statuses.

The server primarily touches test case details and execution records. It’s focused on the core, structured metadata of your testing artifacts.

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