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

Q: How do I integrate Katalon TestOps (AI Test Management) with Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your HTTP endpoint and pass it to your Agent's toolsets array.

Q: How does the agent track execution progress in Katalon TestOps (AI Test Management) via Pydantic AI?

It calls listtestruns to get pass/fail statuses and execution durations. Pydantic validates the integers and strings before the agent processes them.

Bring strict type validation to Katalon TestOps by routing test orchestration through Pydantic AI.

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Connect Katalon TestOps (AI Test Management) MCP to Pydantic AI

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

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Perplexity

Type-safe test results via MCP Server

`get_test_result` and `list_test_results` fetch durations, statuses, and error messages that Pydantic validates instantly. If the Katalon API returns an unexpected payload, the agent throws a loud validation error. You never deal with silent data corruption. Your agent knows exactly what a failed test object looks like, ensuring your automated triage logic relies on verified structures fed by the MCP integration.

Audit builds with zero hallucinations

`list_project_builds` and `list_project_releases` return build identifiers, scheduled dates, and aggregated test statistics. The agent maps these perfectly to your internal data models. You trust the release metrics because the framework enforces strict schemas. The agent pulls details with `get_project` and refuses to proceed if required fields are missing.

Execute reruns predictably

`rerun_test_run` initiates a new execution and returns the newly created test run ID. The agent verifies the ID format before calling `get_test_run` to check its status. This prevents the agent from entering a broken polling loop. It uses `list_execution_environments` to confirm the target environment exists before attempting any automated retry.

Setup guide

Set up Katalon TestOps (AI 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": {
        "katalon-testops-ai-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 Katalon TestOps (AI Test Management) tools.",
)

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

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Common questions about Katalon TestOps (AI Test Management) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an `MCPToolset` with your HTTP endpoint and pass it to your Agent's `toolsets` array.

Standard scripts break silently when APIs change. This setup enforces runtime validation on every test status and build metric the agent retrieves.

It calls `list_test_runs` to get pass/fail statuses and execution durations. Pydantic validates the integers and strings before the agent processes them.

Yes. The framework is model-agnostic. You connect your local LLM, and it interacts with the exact same verified tool schemas.

You only need a single endpoint token to establish the MCP connection. The system fetches your build metadata and error messages through an ephemeral V8 sandbox that destroys itself immediately after execution.

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