Figma Design to Test Cases, Automatically (Figma MCP + Qwen Vision + Qdrant RAG) I built an AI-first pipeline that turns a Figma design file into test cases automatically. Design images and JSON are pulled through Figma MCP in batches of 50, with a leaky-bucket rate limiter to stay within API limits. A fixed-schema parser reduces the huge Figma JSON to what matters: screens, dropdowns, hover states, and navigation edges (source to destination). Each screenshot and its JSON slice go to Qwen vision on Groq, which writes a rich summary of the screen. Every screen becomes a chunk in Qdrant, plus one extra chunk for the full end-to-end flow. At query time, metadata search routes each request to exactly the chunks it needs. Extract: Access token and file ID go through Figma MCP, which returns images and JSON in batches of 50. A leaky-bucket rate limiter keeps requests smooth and rate-limit safe.

Parse: The raw Figma JSON exceeds the LLM context window, so a fixed-schema parser pulls out only the useful parts: dropdowns, hover states, and navigation edges (for example, Search → Product on a result card click).

Understand: Each screen’s screenshot and JSON slice go to Qwen vision on Groq, which produces a detailed summary of the screen, its dropdowns, and its interactions.

Chunk and store: Every screen becomes a chunk in Qdrant, holding the summary, JSON, and metadata (screen_id, flow, source → destination), plus one full-flow chunk (-1).

Retrieve by intent: Metadata search decides what to fetch. For example, “end-to-end test cases” fetches the full-flow chunk, “Login → Search product” fetches only that screen range, and a query with no scope is processed batch by batch.

Generate: The retrieved context is used to generate test cases. Tools to tag Python, Figma MCP, Groq, Qwen Vision, Qdrant, RAG, Multimodal LLMs, Kafka, Rate Limiting, Test Automation, Generative AI