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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—meaning the resulting rule is mathematically tuned 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: