Tewip MCP server

Tewip

Tewip (Uyghur téwip, تېۋىپ: healer, doctor) is the layer that proves software work - written by a person or by an agent.

Created and maintained by Mamuti Aishan (Hasan) (@hmamut39).

It finds why each failing test failed (a real bug, a flaky test, the environment, test data, or a changed selector), says whether a test could ever fail at all, breaks your code to find out whether the tests would notice, reads what a migration will do to a live database, and gives one verdict on whether a change is done. Every answer carries its evidence and names what decided it: a deterministic rule, the AI model, or a person's correction.

What makes it worth installing is what it refuses to say. It answers "cannot say" rather than guessing, it will not narrow a test run it cannot prove safe to narrow, and every report ends with what it did not check. Each number below was measured on strangers' real code, never on fixtures.

It works for every stack: Node.js (node:test, Jest, Vitest, Mocha), Python (pytest, unittest), Java and Kotlin (JUnit, TestNG), Go, .NET, Ruby, PHP and Rust, each checked against its real runner's report (the full list), plus Playwright in depth with traces, selector drift and verified fixes. It also reads the failure wording of the browser and component-test tools on top of those runners (Selenium, WebdriverIO, Cypress, Testing Library for React, Vue and Angular, Vue Test Utils, jest-dom and Jasmine), checked against more than 500 real messages from public projects.

Formerly triage-agent: the repository moved to hmamut39/tewip, and GitHub redirects the old address. See ROADMAP.md for the plan.

Status: phases 1 to 14 are built; the two auto-fix phases (5 and 8) are the ones still in progress. Tewip collects failures from any framework, diagnoses them with rules first and a model only for what rules cannot settle, and turns that into work for each role: a CI comment and merge gate, test plans and verified tests, a flake dossier, a PR-risk review, security scanning, tickets in Jira, Rally, GitHub Issues, Azure Boards or Linear, checks generated from a Figma design, specifications written for people, and - opt in, and only when proved - fixes to test or application code. It runs live in GitHub CI and answers questions from AI assistants through MCP. On fresh real Gutenberg CI failures, with the pipeline frozen before labelling, accuracy is 93.4%; across six real datasets it ranges from 86.1% to 99.7%, and the caveats are in eval/README.md.

What it costs

Shortened. The full README is on GitHub.