Every issue carries reproduction steps, expected versus observed behaviour with specific assertions, screenshots and DOM snapshots — the context a senior QA engineer would hand you. A coding agent given a generic bug report guesses; given that, it can locate the code and write a targeted fix. Their documented batch pattern is to pull every approved issue and open a separate PR for each, which is only sane because the context per issue is that complete. Note the human gate stays in the loop: you approve issues before anything gets fixed.
Pie is an autonomous end-to-end QA platform that tests your app like a user. The endpoint is how a coding agent reaches the results — the vendor's own framing is that this is the point of the integration, not a side feature.
- Test cases — list, create in plain English, run, update, archive, add to a suite, find duplicates, import from CSV
- Issues — review, filter, approve, reject, resolve, and cross-run duplicate analysis
- Results and findings, test runs, discovery triggers, suites and feature groups
- Credentials and scripts for test data, plus app config, health check and reporting
- Admin-only tools sit on top of the regular user surface
An X-API-Key header. Passwords are never returned — get_credentials gives back ids, names and usernames only. Full parameter schemas are in their tool reference.
Paste a URL with a key — add the endpoint to your client