# ContextQA MCP for AI Agents MCP

> ContextQA lets you take full control of context-aware AI testing right from your chat interface. Manage entire test suites, trigger live runs across multiple environments, and inspect complex API payloads using natural conversation. It’s designed for QA engineers and DevOps teams who need deep visibility into automated software quality assurance.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Tags:** automated-testing, ai-healing, test-automation, qa-testing, software-quality

## Description

ContextQA connects the complexity of modern application testing to simple conversation with any AI agent. Instead of logging into a separate dashboard or writing boilerplate scripts, you manage your entire test lifecycle right where you work. You can ask your AI client to list all available projects and then immediately dispatch live tests against them. Need to check if an API endpoint meets OpenAPI standards? Just ask. The platform also monitors active runs, letting you inspect the specific results of AI-healing attempts—showing exactly where a test failed or what structural change caused it. By connecting this MCP via Vinkius, your agent gains direct access to thousands of other development tools, making comprehensive software quality assurance accessible through plain language commands.

## Tools

### list_projects
Lists bounded ContextQA environments that hold groups of automated validations.

### get_project
Retrieves specific Project mapping UUIDs for analyzing execution spaces.

### list_suites
Performs structural extraction matching asynchronous GUI test Suites payloads.

### list_cases
Discovers explicit routing limits that structure ContextQA case definitions.

### get_case
Validates Data Science object extraction and tracks specific test steps boundaries.

### list_executions
Inspects deep internal interactions, tracking global run data chunks.

### get_execution
Executes static queries targeting specific AI-healing test run states.

### list_environments
Lists configured environments, mapping target layers and testing limits.

### list_api_tests
Extracts native REST and OpenAPI testing configuration details.

### trigger_run
Dispatches a live command to execute specific jobs against defined test pipelines.

## Prompt Examples

**Prompt:** 
```
I need to see all bounded environments for our mobile app project.
```

**Response:** 
```
**ContextQA Project Environments**

*   `mobile-app-staging` (Active, Last Run: 2 hours ago)
*   `mobile-app-prod` (Archived, Last Run: Yesterday)
*   `mobile-api-sandbox` (Testing, Active)

You can request a run on any of these environments using the `trigger_run` tool.
```

**Prompt:** 
```
Why did the last execution for project 'vinkius-app-prod' fail?
```

**Response:** 
```
**Analysis Report: vinkius-app-prod (Execution ID: exec_123)**

*   **Failure Step:** 5. Click Login Button.
*   **Root Cause:** Element not found. ContextQA AI-healing attempted location but failed due to a DOM structural change in the main login container.
*   **Recommendation:** Review front-end code for selectors on the login button element.
```

**Prompt:** 
```
Can you list all API test configurations I have?
```

**Response:** 
```
**OpenAPI Assertions Found (via `list_api_tests`)**

| Endpoint | Method | Schema Coverage | Last Verified |
| :--- | :--- | :--- | :--- |
| `/user/profile` | GET | Full Payload Check | 2026-06-10 |
| `/payment` | POST | Partial (Schema Only) | N/A |
| `/inventory` | PUT | Full Payload Check | 2026-05-01 |
```

## Capabilities

### Manage Test Environments and Projects
List defined test environments and group automated validations into projects.

### Map GUI Test Suites
Extract the structure of user interface (GUI) test suites across different project boundaries.

### Run Live Automated Tests
Dispatch live testing commands to queue entire test suites against ContextQA clusters directly from your chat.

### Audit API and Swagger Payloads
Enumerate automated HTTP assertions and verify structural data payloads against OpenAPI configurations.

### Monitor AI-Healing Failures
Inspect detailed test runs to view specific AI-healing states, including failure boundaries and screen captures.

### Verify System Boundaries
List physical runtime URLs and group active contexts to verify testing scope across different application layers.

## Use Cases

### Investigating a Broken Checkout Flow
A QA Engineer notices the checkout flow is failing intermittently. Instead of logging into three different tools, they ask their agent to run `list_suites` for the 'Checkout-Flow', then use `get_execution` on the failed run ID to pinpoint if the issue was a structural DOM change or an authentication failure.

### Validating New Microservice APIs
A developer needs to confirm that a new payment API endpoint adheres exactly to the OpenAPI spec. They use `list_api_tests` and ask their agent to verify the payload structure against the expected schema, getting instant confirmation on success or failure.

### Auditing Release Readiness
A Product Owner is concerned about a release candidate. Using `list_projects`, they can see all bounded test environments and then use the MCP to trigger comprehensive runs across multiple critical areas, monitoring the overall health before sign-off.

### Debugging Environment Discrepancies
A DevOps team member suspects a bug only appears in staging. They use `list_environments` and group active contexts to verify that all layers—frontend, backend, and database connections—are pointing to the correct target URLs for accurate testing.

## Benefits

- Stop switching between dashboards. You can monitor active test runs and inspect specific AI-healing states, like failing steps or screen captures, all from your chat.
- Validate complex APIs instantly. Use this MCP to enumerate automated HTTP assertions and verify structural payloads against OpenAPI configurations without writing a single line of code.
- Control the entire lifecycle. Easily list bounded test environments using `list_projects` and dispatch live testing commands with `trigger_run`, all through natural language conversation.
- Deep visibility into failures. Use `get_execution` to query specific AI-healing states, helping you find the precise root cause of a failure that was hard to track manually.
- Comprehensive coverage mapping. List physical runtime URLs using `list_environments` to verify testing boundaries across multiple application layers before deployment.

## How It Works

The bottom line is you get full command-line control over sophisticated automated testing workflows using only natural conversation prompts.

1. Subscribe to the ContextQA MCP on Vinkius.
2. Enter your unique ContextQA API Key into your AI client's settings.
3. Ask your agent to perform a task, like listing test suites or triggering a run.

## Frequently Asked Questions

**How does ContextQA help me debug a failing test run?**
ContextQA provides deep visibility into failures by showing you the AI-healing state, which tracks exactly why an element wasn't found or what structural change caused the failure. It tells you more than just 'failed'; it tells you *why* it failed.

**Do I need to write code to test my API endpoints?**
No. You don't write code; you use ContextQA to enumerate automated HTTP assertions and verify payloads against OpenAPI configurations using natural conversation. It handles the technical complexity for you.

**What is 'AI-healing' in the context of ContextQA?**
AI-healing refers to the platform's ability to detect when a test breaks due to small changes (like a button moving) and attempt to automatically adjust the test logic. You can inspect these attempts using specific execution tools.

**Can ContextQA manage multiple testing environments?**
Yes, it lists multiple bounded test environments using `list_environments`. This lets you ensure that whether you are testing on staging or pre-prod, the context and boundaries are set correctly every time.

**Is ContextQA only for GUI tests?**
Not at all. While it manages complex GUI suites, it also specializes in backend quality assurance by verifying structural payloads against OpenAPI configurations and running API assertions.