Can my agent help me identify the root cause of a failed test case?

Yes. Use the 'get_case' tool to resolve the AI root-cause model for a specific test. The agent retrieves the definitions and AI insights to confirm exactly why a boundary was breached during execution.

How do I trigger a full test suite run via chat?

Provide the 'project_id' and 'suite_id' to your agent and use the 'trigger_run' mutation. The agent will command the backend to queue the tests against live ContextQA test clusters instantly.

Can I see screen captures of failed test steps through the agent?

The 'get_execution' tool retrieves detailed logs tracking failing step boundaries. Where supported by ContextQA, the agent can surface the associated physical screen capture limits to help you visualize the failure.

ContextQA MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: ContextQA
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ContextQA through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to ContextQA "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in ContextQA?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ContextQA MCP Server

Connect your ContextQA account to any AI agent and take full control of your context-aware AI testing platform through natural conversation.

Pydantic AI validates every ContextQA tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Project & Suite Management — List bounded test environments and perform structural extraction of GUI test suites across your projects
AI-Healing Executions — Monitor active test runs and inspect specific AI-healing states, including failing step boundaries and screen captures
Automated Triggers — Dispatch live testing commands to queue suites against ContextQA test clusters directly from your workspace
API & Swagger Testing — Enumerate automated HTTP assertions and explicitly verify structural payloads against OpenAPI configurations
Environment Auditing — List physical runtime URLs and group active contexts to verify testing boundaries across different layers
Test Case Inspection — Resolve AI root-cause models and validate specific case definitions to identify precise points of failure

The ContextQA MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect ContextQA to Pydantic AI via MCP

Follow these steps to integrate the ContextQA MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from ContextQA with type-safe schemas

Why Use Pydantic AI with the ContextQA MCP Server

Pydantic AI provides unique advantages when paired with ContextQA through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your ContextQA integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ContextQA connection logic from agent behavior for testable, maintainable code

ContextQA + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the ContextQA MCP Server delivers measurable value.

Type-safe data pipelines: query ContextQA with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ContextQA tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ContextQA and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ContextQA responses and write comprehensive agent tests

ContextQA MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect ContextQA to Pydantic AI via MCP:

get_case

Validate Data Science object extraction tracking explicit steps boundaries

get_execution

Execute static queries targeting exactly specific AI-healing Run states

get_project

Retrieve explicit Project mapping UUIDs analyzing execution spaces limitlessly

list_api_tests

Extracts native REST & OpenAPI testing configurations natively

list_cases

Discover explicit routing limits structuring ContextQA cases trees

list_environments

List static configurations mapping Environment target layers mapping limits

list_executions

Inspect deep internal interaction tracking explicit global Run chunks

list_projects

Identify bounded ContextQA test environments grouping automated validations

list_suites

Perform structural extraction matching asynchronous GUI test Suites payloads

trigger_run

Dispatch a live testing command routing explicit Jobs against pipelines

Example Prompts for ContextQA in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with ContextQA immediately.

"List all test suites for project 'vinkius-app-prod'"

"Trigger a run for suite 'Checkout-Flow' in project 'vinkius-app-prod'"

"Show me why the last execution of project 'mobile-app' failed"

Troubleshooting ContextQA MCP Server with Pydantic AI

Common issues when connecting ContextQA to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ContextQA + Pydantic AI FAQ

Common questions about integrating ContextQA MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your ContextQA MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.