Cursor alone has no project-specific context, cannot control what it asks the user, and is unreliable on large repositories. GenieTest closes those gaps with a custom MCP server, Cursor rules for prompt engineering, a Cursor extension and a purpose-built RAG dataset, so QE engineers get test automation scripts faster.
The data pipeline chunks the team's test repositories into a test functions database (description, steps, dependencies) and a helper functions database (usage patterns). OpenAI's Batch API did the initial extraction, the output was quality-checked by hand, and embeddings come from a hosted Snowflake Arctic model.
The MCP server exposes the tools: a steps generator breaks the request into structured steps, the user reviews them, a vector search finds the closest existing test, and a code generator then reuses that test, or writes a new test in the same or a new class, pulling helper functions per step. A review and edit tool does the same for changes to an existing test.
Write-up (PDF)
- Tools
- Steps generator, display steps, closest test, code generator, review and edit
- Retrieval
- Vector search over test and helper function databases
- Host
- Cursor IDE with MCP client and Cursor rules
- Built with
- Python MCP server, Snowflake Arctic embeddings, OpenAI Batch API