How to Use the Mabl (AI-Powered Test Automation) MCP in Pydantic AI

Q: How do I register the Mabl (AI-Powered Test Automation) MCP Server with Pydantic AI?

Install the slim package with MCP support and instantiate MCPToolset with the HTTP server URL. Pass this object into the toolsets argument of your Agent constructor to automatically register all tools.

Build type-safe Pydantic AI workflows that trigger Mabl test plans and validate execution results at runtime with absolute certainty.

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Connect Mabl (AI-Powered Test Automation) MCP to Pydantic AI

Create your Vinkius account to connect Mabl (AI-Powered Test Automation) 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 Mabl (AI-Powered Test Automation) with Pydantic AI

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Enforce type-safe testing runs using Pydantic AI

Stop worrying about your agent passing incorrect arguments to your tests. When using the Mabl MCP Server, every input and output is validated against strict Pydantic models before execution. If the agent attempts to trigger a run with an invalid environment ID via `mb.trigger_plan`, the framework catches it instantly. You get a clear validation error instead of a silent failure in your CI pipeline.

Validate execution results and failure metrics

Parse test outputs with complete confidence. The agent uses `mb.get_execution` to retrieve detailed run results, verifying that the returned JSON matches your expected schema. Every metric like duration, failure analysis, and test outcome is guaranteed to be perfectly typed. Your agent can safely pass this structured data to down-stream reporting tools without parsing errors.

Map application targets and labels safely

Discover your testing infrastructure without typing mistakes. The agent calls `mb.list_apps` and `mb.list_envs` to populate its target registry with validated data. It cross-references these targets with tags from `mb.list_labels` to ensure only verified test plans are executed. This prevents the agent from running obsolete tests on active staging environments.

Setup guide

Set up Mabl (AI-Powered Test Automation) 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": {
        "mabl-ai-powered-test-automation-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Mabl (AI-Powered Test Automation) transactions")
print(result.output)

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Common questions about Mabl (AI-Powered Test Automation) MCP in Pydantic AI

Install the slim package with MCP support and instantiate `MCPToolset` with the HTTP server URL. Pass this object into the `toolsets` argument of your `Agent` constructor to automatically register all tools.

Yes, you can. Your agent can query available test suites with `mb.list_plans` and execute them via `mb.trigger_plan`. Every parameter passed to the trigger tool is validated at runtime to prevent execution errors.

If Mabl returns unexpected data structure, the framework raises a validation error. This prevents your agent from making decisions based on malformed execution results or corrupted screenshot data.

Yes, the framework is completely model-agnostic. You can connect this server to local models or any major cloud LLM provider, as long as the model supports tool calling.

Your Mabl API keys and workspace metadata are never exposed or hardcoded in the agent's logic. All communication is routed through a secure MCP sandbox, ensuring that sensitive test execution results and environment variables remain encrypted.

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