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@ -25,7 +25,7 @@ When an LLM agent needs to follow these conventions, it usually has two bad opti
2. **Guess from one or two examples** — the LLM infers a pattern and often gets it wrong.
Dervish replaces both with a **one-call MCP tool**: pass your sequences, get back a ~60-token grammar.
By leveraging **Minimum Description Length (MDL) scoring**, Dervish treats the grammar discovery problem as an optimal compression task—meaning the resulting rule is mathematically tuned to consume as few tokens as possible without losing the pattern.
By leveraging **Minimum Description Length (MDL) scoring**, Dervish treats the grammar discovery problem as an optimal compression task. the resulting rule is optimized to consume as few tokens as possible without losing the pattern.
**Without Dervish:** token cost scales linearly with examples. **With Dervish:** one compact grammar describes them all — a ~60–200 token rule instead of thousands of tokens of raw examples. Try it out and you too will say: