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 OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: ContextQA
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect ContextQA through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="ContextQA Assistant",
            instructions=(
                "You help users interact with ContextQA. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from ContextQA"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

The OpenAI Agents SDK auto-discovers all 10 tools from ContextQA through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries ContextQA, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the ContextQA MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from ContextQA

Why Use OpenAI Agents SDK with the ContextQA MCP Server

OpenAI Agents SDK provides unique advantages when paired with ContextQA through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

ContextQA + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the ContextQA MCP Server delivers measurable value.

Automated workflows: build agents that query ContextQA, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries ContextQA, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through ContextQA tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query ContextQA to resolve tickets, look up records, and update statuses without human intervention

ContextQA MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect ContextQA to OpenAI Agents SDK 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK

Common issues when connecting ContextQA to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

ContextQA + OpenAI Agents SDK FAQ

Common questions about integrating ContextQA MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.