For SDETs
Extend your automation framework with 67 MCP tools, export tests as Playwright code, manage test infrastructure via API, and let AI handle the maintenance burden so you focus on architecture.
Who is this for? Software Development Engineers in Test (SDETs), senior automation engineers, and QA engineers who write and maintain test frameworks.
New to ContextQA? Start with Core Concepts for the key terminology used throughout these docs — Workspace, Test Case, Test Suite, Test Plan, and Environment.
You've written the frameworks, built the CI/CD integrations, and maintained the test suites. You know the real cost: every sprint brings selector rot, environment drift, and another afternoon debugging why a locator stopped working. ContextQA augments your existing expertise with AI infrastructure that handles the brittle parts — so you focus on test architecture, coverage strategy, and toolchain integration.
New to ContextQA? Read Core Concepts first to get familiar with the key terms used throughout these docs — Workspace, Test Case, Test Suite, Test Plan, and Environment.
What ContextQA Adds to Your Stack
67 MCP tools
Full platform control from Claude, Cursor, or any MCP-compatible AI agent
export_to_playwright
Export any ContextQA test as runnable Playwright TypeScript code
export_test_case_as_code
Get the raw step definitions for custom framework integration
AI self-healing
Zero selector maintenance — AI fixes broken locators above 90% confidence
Evidence API
Programmatic access to screenshots, HAR, console logs, Playwright traces
Parallel execution
Run full regression in minutes across browsers and devices
CI/CD ready
Plan-based quality-gate pattern works across common pipeline tools
MCP Server Integration
ContextQA exposes a Model Context Protocol server at your configured endpoint. Every platform capability is available as a tool call from any MCP-compatible AI client.
Key SDET tools:
Exporting Tests as Playwright Code
Any test case created in ContextQA can be exported as Playwright TypeScript for use in your existing framework:
The exported code includes:
Page object model structure
Resilient locator strategies (role-based + text-based + attribute fallbacks)
Explicit wait patterns matching ContextQA's execution behavior
Assertion calls using Playwright's
expect()API
CI/CD Integration Patterns
Use the same plan-based quality-gate design across GitHub Actions, Jenkins, GitLab CI, CircleCI, Azure DevOps, and other supported pipeline systems:
Manually validate the plan and environment before making it a required gate.
Store a dedicated API token in the CI provider's secret store.
Start the plan using the current public contract supplied for the workspace.
Keep the returned run ID and check status with a bounded timeout.
Map every documented non-success result to a failing job outcome.
Include the run ID in job output for traceability.
The verified production CI / CD tab describes start and status operations but does not display public endpoint paths. Do not infer a contract from portal network traffic or reuse legacy /testplans/{id}/execute examples.
→ GitHub Actions | Jenkins | GitLab CI
Test Architecture Best Practices
Step Groups as Reusable Libraries
Build a SG_Auth step group containing your login flow. Reference it in every test case that requires authentication. When the login form changes, update SG_Auth once — all test cases inherit the fix automatically.
Environments for Multi-Stage Testing
Define staging, qa, and production environments with their respective base URLs and API keys. Test Plans reference an environment by name — the same plan runs against any stage without modification.
Knowledge Base for Application Context
Add known UI quirks to the Knowledge Base:
"Always dismiss the cookie consent banner before interacting with the page"
"The loading spinner takes up to 8 seconds on the checkout page"
"Use credentials testuser@corp.com / TestPass123 for MFA bypass in staging"
The AI reads these instructions before every execution — reducing false failures from environment-specific behavior.
Custom Agents for Domain Logic
Create a Custom Agent with a tailored system prompt for complex scenarios:
A Salesforce-aware agent that understands Lightning UI navigation patterns
An accessibility agent that verifies ARIA labels on every step
A performance agent that flags any network request exceeding 2 seconds
Evidence & Debugging API
Every execution produces a queryable evidence package:
get_test_step_results
Per-step pass/fail, screenshot URL, assertion detail
get_console_logs
Browser console entries (errors, warnings, info)
get_network_logs
Full HAR network log for the execution
get_trace_url
Playwright trace viewer URL (.zip downloadable)
get_root_cause
AI classification + suggested fix + affected step number
get_ai_reasoning
Full AI reasoning chain for the execution
get_ai_insights
Pattern-based insights across multiple executions
Flaky Test Management
ContextQA automatically classifies failures across four categories:
Test bug — the test assertion is incorrect
Application bug — the application has a regression
Flaky failure — the test passes on retry, likely a timing issue
Environment issue — infrastructure or network problem
Use get_root_cause to retrieve this classification programmatically and route failures to the correct team automatically.
Recommended next steps for SDETs:
MCP Server Installation — connect your AI agent in 10 minutes
Tool Reference — full 67-tool catalog with parameters
Agent Integration Guide — Claude/Cursor integration patterns
CI/CD integrations — drop-in pipeline configs
See the platform from an SDET's perspective. Book a Technical Demo → — A 45-minute deep-dive into MCP tooling, API patterns, and CI/CD integration with your actual test infrastructure.
SDETs using ContextQA report 70% less time spent on test maintenance.
Last updated
Was this helpful?