qikly MCP server

qikly

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The problem: Your AI writes both the code and its tests. How do you know the tests are really valid?

The solution: two agents. One turns the acceptance criteria into tests. The other writes the code and never sees the acceptance criteria.

Who it is for: a developer or team pointing an AI coding agent at a self-contained Python module that transforms data, for example an ETL step, a merge, a calculation or a validation routine, who does not want to trust a green suite when the same agent wrote both the code and the tests. It suits one module at a time in small to mid-sized repositories: when a test fails, only the files that failure names are loaded, so runs stay small and quick. It fits most naturally where verification already has to be independent, such as automotive, medical devices, fintech and defence: ADAS_HEADWAY, a bundled example, checks following distance from forward-radar samples. See What it is for.

Just want to see how it works?

qikly --explain CALC_TAX: it prints what each agent is shown, and the difference. More on the free commands.

• Free, and with no API key. Run pip install qikly, then

quick start on the demo task, which converges a real run in about half a minute for well under a cent.

• With an API key, for Gemini, Claude or OpenAI:

Just want to try it on your own data? Quick start on your own data, five steps from your module to a first run.

The idea

Imagine a student who writes the exam paper, writes the answer key, and then sits the exam. They pass, and nobody would accept that as evidence they know the material. That is what happens when one model gets a specification containing the acceptance criteria and writes both the code and the suite that checks it: everything goes green, and the green means nothing.

qikly takes the answer key away from the student. It generates a test suite from the acceptance criteria, then writes an implementation and repairs it against that suite until every test passes or a retry budget runs out, recording every failure, every piece of reasoning and every diff.

Shortened. The full README is on GitHub.