# Dervish MCP

<p align="left"><img src="dervish-logo.png" alt="Dervish" width="180"></p>

**Dervish** infers **regular expression grammars** from example sequences using the BEX family of algorithms. Given a set of example sequences (strings over some alphabet), it learns a compact regular expression that captures the general pattern.

Every codebase has unwritten conventions — the order tasks appear in Ansible roles, the resources a Helm chart always creates, the steps every CI pipeline runs. Nobody writes these down. They emerge from copying and converging.

When an LLM agent needs to follow these conventions, it usually has two bad options:

1. **Stuff every existing file into context** — 15 Ansible roles = 5,000 tokens. You'll hit the context window by the third example.
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. A rule you can trust, at a fraction of the cost.

**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.

<p align="center"><img src="dervish.gif" alt="Dervish animation" width="37.5%"></p>

## MCP Server

The primary interface is a **Model Context Protocol (MCP)** server. Connect any MCP-compatible client (pi.dev, opencode, vibe, etc.) and get grammar inference as a tool:

```json
{
  "mcpServers": {
    "dervish": {
      "command": "python3",
      "args": ["/path/to/bex/mcp_server.py"]
    }
  }
}
```

### Tools

| Tool | Parameters | What it does |
|------|-----------|-------------|
| `infer_best_grammar` | `sequences`, `prefer`, `kmax`, `N` | **Recommended.** Runs CRX + iDRegEx, picks best by MDL. Set `prefer='crx'` or `prefer='idregex'` to run one algorithm. |
| `infer_grammar` | `sequences`, `method`, `kmax`, `N` | Core single-algorithm inference. `method='crx'` (fast, deterministic) or `method='idregex'` (probabilistic EM). |

**Parameters explained:**
- **`kmax`** (1–5): Context window for iDRegEx's k-testable automaton. Higher values capture longer-range dependencies but need more data and are slower. Default 2 works for most cases.
- **`N`** (1–10): Baum-Welch EM iterations for iDRegEx training. More iterations = better convergence but slower. Default 3 is a good balance.
- **`prefer`**: Skip the CRX-vs-iDRegEx comparison. Use when you know which algorithm fits your data.

### Agent workflow

An LLM agent uses the MCP to discover an unwritten convention from existing examples — compressing hundreds of files into a single ~60-token rule:

```
User: Generate a new Ansible role for installing PostgreSQL.

Agent: Let me check what pattern the existing community roles follow.
       I'll look at 15 popular geerlingguy roles.

       [finds role directories, extracts task module sequences,
        calls infer_best_grammar(sequences=..., prefer='crx')]

       Dervish returns:
         Best: CRX (MDL 288)
         Grammar: fail?.(include_vars+set_fact+package+file+template+service+...)+
                  .include+?.(npm+pip)+?.lineinfile?

       This tells me: every role starts with a fail check for preconditions,
       then OS-specific variables, installs packages, configures with templates,
       starts services, and optionally handles language tooling (npm/pip).
       The role should end with a lineinfile tweak.

       I'll generate the new role following this structure.
```

**Without Dervish:** the agent stuffs 15 role files into context (5,000+ tokens per role = beyond any context window), or guesses the pattern from 1–2 examples and often gets it wrong.

**With Dervish:** one MCP call returns a ~60-token grammar known to match 15/15 existing roles. The agent follows it reliably.

## Quick Start

```bash
pip install pyyaml
python -m bex
```

```python
from bex import infer_ensemble

seqs = [
    ['file', 'template', 'docker_image', 'command', 'set_fact', 'shell', 'wait_for'],
    ['file', 'template', 'docker_image', 'command', 'set_fact', 'shell'],
]

result = infer_ensemble(seqs)
print(f"Best: {result['best']['algorithm']}")
print(f"Grammar: {result['best']['grammar']}")
print(f"Score: {result['best']['mdl_score']}")
```

## Why not just use a schema?

Many of the things developers build every day **have no formal schema**. They're free-form scripts, config files, or YAML blobs where the structure is emergent convention, not enforced specification. An LLM generating new content in these domains needs to know the convention — but it's never been written down.

Dervish discovers these conventions automatically from existing examples. The domains below are **just examples** of what it can do — the same approach works for any sequential data with an unwritten pattern.

| Domain | What gets extracted | Example extracted symbols | What Dervish discovers | Why it helps an LLM |
|--------|-------------------|--------------------------|----------------------|---------------------|
| Ansible roles | Module names from `tasks/main.yml` in order | `fail`, `include_vars`, `set_fact`, `package`, `file`, `template`, `service`, `npm`, `pip`, `lineinfile` | `fail?.(include_vars+set_fact+package+file+template+service+...)+.include+?.(npm+pip)+?.lineinfile?` | "Validate preconditions first, then set vars, install packages, configure with templates, start services. Include sub-roles last." |
| Helm charts | K8s resource kinds from `helm template` output in rendered order | `ServiceAccount`, `ClusterRole`, `ClusterRoleBinding`, `Service`, `Deployment`, `ConfigMap`, `Alertmanager` | `ServiceAccount.ClusterRole.ClusterRoleBinding.Service.Deployment` (iDRegEx minimal core) | "Every Prometheus stack needs this bootstrap pipeline. Everything else is optional." |
| GitHub Actions (Go lint) | Step `uses:` or `run:` values from workflow YAML in job order | `actions/checkout`, `actions/setup-go`, `golangci/golangci-lint-action`, `megalinter/megalinter` | `actions/checkout.(actions/setup-go+run:echo+run:sudo)+.golangci/golangci-lint-action?.megalinter?` | "Starting a new Go project on GitHub Actions? Four independent projects converged on: checkout → setup Go → golangci-lint → (optionally megalinter)." |


## Real-world Results

Dervish has been tested against public datasets from Ansible Galaxy, Helm, and GitHub Actions — all cases where multiple projects independently converged on an undocumented pattern. [**Full details → SHOWCASE.md**](SHOWCASE.md)

| Dataset | Best grammar | Compression |
|---------|-------------|-------------|
| Ansible Galaxy (15 roles) | `fail?.(include_vars+set_fact+package+file+template+service+...)+.include+?.(npm+pip)+?.lineinfile?` | 5,000 tokens → 60 tokens (83×) |
| Helm (6 configs) | `ServiceAccount.ClusterRole.ClusterRoleBinding.Service.Deployment` | ~3,000 tokens → 40 tokens (75×) |
| Go lint (6 jobs) | `actions/checkout.(actions/setup-go+run:echo+run:sudo)+.golangci/golangci-lint-action?.megalinter?` | ~900 tokens → 30 tokens (30×) |

The sweet spot: **multiple implementations of the same abstract task** with a shared but undocumented pattern. Not everything works — Dockerfiles, pre-commit configs, and schema-enforced formats are too rigid or too diverse to yield a convention.

## Algorithm Selection Guide

| When | Use | Why |
|------|-----|-----|
| Clean, structured data with full vocabulary | **CRX** | Single-pass, deterministic. Accepts all sequences. |
| Few examples, or want minimal common core | **iDRegEx** | Probabilistic EM, finds only what's shared. |
| Don't know which is better | **Ensemble (default)** | Runs both, picks the best by MDL score. |
| Data is clearly one type | `prefer='crx'` or `prefer='idregex'` | Skips ensemble comparison, runs one algorithm. |

## When each algorithm wins

| Data property | Winner | Why |
|---------------|--------|-----|
| Diverse patterns, full vocabulary needed | CRX | Captures all symbols. iDRegEx returns ∅. |
| Clean sequences with clear core | iDRegEx | Extracts minimal common subsequence. CRX buries it in optional noise. |
| Single sequence | iDRegEx (+ RWR₀) | RWR₀ repair produces a grammatical regex from one example. |
| 2–3 sequences | iDRegEx | CRX overfits. iDRegEx handles noise better. |
| Many sequences, tight pattern | CRX | Learns precise concatenation with optional suffixes. |

## Token savings

<p align="center">
  <img src="chart_token_savings.png" alt="Token savings per dataset" width="75%">
</p>

Across all public benchmarks, Dervish delivers **40–83× compression**. The grammar is smaller than a single example file would be — and it represents the entire dataset.

## How MDL scoring works

```
MDL = model_cost + data_cost
```

- **model_cost** — number of unique alphabet symbols in the grammar. Simpler grammars are cheaper.
- **data_cost** — Σ log₂(|L(r) at length len(s)|) across all sequences. A specific fixed sequence (`a.b.c.d.e`) has data cost zero because |L(r)| = 1. A grammar that accepts *many* strings of the same length (like `(a+b+...+q)+`) has high data cost.

The ensemble selects the grammar with the lowest total MDL.

## Grammar Notation

- `a.b` — `a` followed by `b` (concatenation)
- `(a+b)` — either `a` or `b` (disjunction)
- `r?` — zero or one (optional)
- `r+` — one or more (iteration)
- `r+?` — zero or more (varies across examples)

## Papers

- **Bex et al.** *[Learning Deterministic Regular Expressions for the Web](https://doi.org/10.1145/1806907.1806911)* — TODS 2010
- **Bex et al.** *[Simplifying XML Schema: Single-Type Approximations of Regular Expressions](https://arxiv.org/abs/1004.2372)* — arXiv:1004.2372

## Tests

```bash
python -m pytest tests/
```

## License

MIT
