Matryoshka MCP Server

From the README

# Matryoshka

Process documents 100x larger than your LLM's context window—without vector databases or chunking heuristics.

## The Problem

LLMs have fixed context windows. Traditional solutions (RAG, chunking) lose information or miss connections across chunks. RLM takes a different approach: the model reasons about your query and outputs symbolic commands that a logic engine executes against the document.

Based on the [Recursive Language Models paper](https://arxiv.org/abs/2512.24601).

## How It Works

Unlike traditional approaches where an LLM writes arbitrary code, RLM uses **[Nucleus](https://github.com/michaelwhitford/nucleus)**—a constrained symbolic language based on S-expressions. The LLM outputs Nucleus commands, which are parsed, type-checked, and executed by **Lattice**, our logic engine.

```
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   User Query    │────▶│   LLM Reasons   │────▶│ Nucleus Command │
│ "total sales?"  │     │  about intent   │