How to Use the Xray (Test Management) MCP in Pydantic AI

Q: Can Pydantic AI list all available tests for Xray (Test Management)?

Yes, listxraytests provides a type-safe list of test cases. You'll get an array of objects where every object adheres to the expected schema.

Q: Does the Pydantic AI allow getting comprehensive Xray (Test Management) details?

Absolutely. gettestdetails pulls all the necessary test case information, and because of the type validation, you never have to worry about missing or malformed fields.

Q: What is the privacy handling for Xray (Test Management) with Pydantic AI?

The server touches project configuration data (getxraysettings). The typed nature of the toolset means your agent only processes and validates known, expected metadata fields.

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Connect Xray (Test Management) MCP to Pydantic AI

Create your Vinkius account to connect Xray (Test Management) 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 Xray (Test Management) with Pydantic AI

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Check detailed test run results.

The `get_execution_details` tool retrieves granular data for a specific test execution. Because the response is validated against Pydantic models, you know exactly what fields to expect. This prevents silent corruption; if Xray sends unexpected output, your agent fails immediately with an error.

Manage and list all test plans.

Use `list_test_plans` to get a structured list of every configured plan. The Pydantic validation ensures that the plan objects returned are always correct. Your agent can trust this data, which is crucial for automated decision-making based on testing requirements.

Retrieve individual test run history.

The `get_individual_test_runs` tool gives you instance-level data for a single test. The runtime validation makes sure that every metric and timestamp field is present. This reliability means your agent can build accurate audit trails without guessing what the API might return.

Setup guide

Set up Xray (Test Management) 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": {
        "xray-test-management-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Xray (Test Management) transactions")
print(result.output)

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Common questions about Xray (Test Management) MCP in Pydantic AI

Your agent calls Xray tools, but every single response gets validated against Python types. This means you get reliable data structures whether you're using OpenAI, Anthropic, or Gemini models.

Yes, `list_xray_tests` provides a type-safe list of test cases. You'll get an array of objects where every object adheres to the expected schema.

This server deals with structured metadata: test plan definitions, execution results, and project settings. The focus is on validating these specific Jira quality assurance fields.

Absolutely. `get_test_details` pulls all the necessary test case information, and because of the type validation, you never have to worry about missing or malformed fields.

The server touches project configuration data (`get_xray_settings`). The typed nature of the toolset means your agent only processes and validates known, expected metadata fields.

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