# optionality-mcp [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/lonniev/optionality-mcp  
**GitHub Stars:** 0  
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**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/optionality-mcp

## Description
Optionality — AI-judged options trading drill, Tollbooth-monetized MCP server

## Claude Desktop Quick Installation
Remote MCP endpoint (confidence: high). Install path detected from listing signals. Add as a URL/SSE server in your client:

```json
"mcpServers": {
  "optionality-mcp": {
    "url": "https://modelcontextprotocol.io/"
  }
}
```

## Documentation & README

# optionality-mcp

[MCP](https://modelcontextprotocol.io/) server and React drill UI for an AI-judged options trading practice game. Built on [FastMCP](https://github.com/jlowin/fastmcp).

Optionality is an instance of a [Tollbooth-DPYC](https://tollbooth-dpyc.com)™ service: engagement is monetized with convenient Don't Pester Your Customer™ (DPYC™) Bitcoin commerce. Patrons pre-fund a balance over Lightning and play without per-request payment ceremonies. All prices are dynamic and set by the operator — the Welcome page shows live quotes. Patrons can also enter Tollbooth-DPYC coupons to take advantage of discounts when they are offered.

## How a Round Works

1. **The Dealer deals.** A *dealer* LLM composes a complete options scenario: ticker, spot, IV regime and skew, macro backdrop, catalyst, key levels, and constraints — optionally including a max-loss budget the structure must fit.
2. **You pitch.** Free text, the way you'd pitch a senior PM. Multi-leg structures, single legs, or a deliberate stand-aside — declining to trade is a legitimate, gradeable answer.
3. **The Judge grades.** A *judge* LLM parses your pitch into structured legs, scores it across six dimensions, and proposes an alternative structure you can overlay on the risk chart.

**Six judging dimensions:** Strategy Selection, Strikes & Tenor, Risk/Reward, Macro Integration, Tail Risk, and Communication — each 0–20, rolled into a 0–100 score with letter grades A+ through F.

## Core Pedagogy — Red Herrings

Each scenario embeds 1–2 facts that are factually TRUE but immaterial, woven inline into the narrative and never flagged. Citing them as trade drivers penalizes the trainee; recognizing them as noise and setting them aside earns points. The drill is signal-from-noise on a tape where everything you read is true.

A **Facts Ledger** accompanies every evaluation: which scenario facts you integrated, which you missed, which red herrings you caught, and which you followed.

## Scenario Modes & Difficulty

**Three historicity modes:**

- **Historical Fiction** — real, identifiable market moments (SVB week, the gilt crisis), grounded in the actual macro and IV regime of the day
- **Fiction** — invented regimes: counterfactual shocks, de-peg cascades, gamma squeezes
- **Live Events** — web-search-grounded scenarios anchored to this week's actual tape, with cited sources

**Four difficulty personas:** Apprentice, Journeyman, Adept, Sovereign. Leaderboard points are difficulty-weighted, so rankings can't be padded on easy mode. A **Mulligan** mode replays an already-judged scenario fresh.

## Options Math — One Source of Truth

The server builds the full option chain from the dealer's scaffold — three expirations, a strike ladder around spot, a three-anchor IV smile honoring put-bid skew — and prices it with Black–Scholes. The same math runs client-side, so the trainee, the charts, and the judge all see identical numbers.

- **Option chain modal** in broker convention: calls left, strikes and smile center, puts right; tap a mid to buy or sell; running net-premium readout
- **Risk profile chart** with expiration P/L, breakeven markers, and a DTE slider that replays theta bleed across the holding period
- **Judge-alternative overlay** to compare your payoff curve against the structure the judge would have run

## Socratic Clue Desk

Mid-scenario, ask anything. Educational questions get direct, formula-backed answers; tactical questions get redirected to the dimension worth more thought — the responsibility stays with the trainee. The desk never reveals the scenario's hidden facts or red herrings. Clues carry a scoring penalty.

## Journal, Leaderboard & Peer Learning

- **Journal** — every round persisted: open drafts, submitted pitches, full evaluations with leg tables and charts
- **Leaderboard** — six sort orders (weighted average, weighted best, raw average, raw best, streak, played), filterable by mode and difficulty
- **Streaks** — consecutive scores of 70+, current and all-time
- **Shared entries** — opt in to share an evaluated round so others can study the pitch, the grade, and the ledger
- **Profile** — display name, avatar, bio
- **Usage** — transparency tab showing per-model token consumption, per-tool spend, and the patron's account statement

## Repo Layout

```
optionality-mcp/
├── server.py    # FastMCP SSE server (Python) → Horizon
├── tools/       # dealer, judge, journal, leaderboard, profile, options chain
├── prompts.py   # dealer / judge / clue-desk personas
└── frontend/    # React 18 + Vite + TS UI → Cloudflare Pages
```

Heavy LLM tools (deal, judge, clue desk) use a claim-check async pattern: the call returns a claim immediately and the client polls a free fetch tool, so slow generations survive client timeouts.

## DPYC Ecosystem

`optionality-mcp` is one Operator in the DPYC federation — independent MCP servers that share a Nostr identity model, Bitcoin Lightning payments, and the `tollbooth-dpyc` SDK. Peer repos:

| Repo | Role |
|---|---|
| [tollbooth-dpyc](https://github.com/lonniev/tollbooth-dpyc) | Python SDK — vault, auth, pricing, Lightning, Nostr identity |
| [dpyc-community](https://github.com/lonniev/dpyc-community) | Governance registry: membership, advisories, threat model |
| [dpyc-oracle](https://github.com/lonniev/dpyc-oracle) | Community concierge (free onboarding + member lookup) |
| [tollbooth-authority](https://github.com/lonniev/tollbooth-authority) | Certification backbone (Schnorr-signed certificates) |
| [tollbooth-sample](https://github.com/lonniev/tollbooth-sample) | Sample Operator (canonical template) |
| [tollbooth-pricing-studio](https://github.com/lonniev/tollbooth-pricing-studio) | iOS pricing-model editor / operator console |
| [cypher-mcp](https://github.com/lonniev/cypher-mcp) | Monetized graph answers: named Cypher templates over Neo4j/AuraDB |
| [schwab-mcp](https://github.com/lonniev/schwab-mcp) | Charles Schwab brokerage data |
| [thebrain-mcp](https://github.com/lonniev/thebrain-mcp) | TheBrain personal knowledge graph |
| [excalibur-mcp](https://github.com/lonniev/excalibur-mcp) | X/Twitter posting |
| [taxsort-mcp](https://github.com/lonniev/taxsort-mcp) | Tax classification + Cloudflare Pages UI |
| [optionality-mcp](https://github.com/lonniev/optionality-mcp) | Options analytics (brokerage-data Operator) |
| [tollbooth-oauth2-collector](https://github.com/lonniev/tollbooth-oauth2-collector) | OAuth2 callback handler (advocate service) |
| [tollbooth-shortlinks](https://github.com/lonniev/tollbooth-shortlinks) | URL shortener utility |

## License

Apache 2.0 — see [LICENSE](https://github.com/lonniev/optionality-mcp/blob/HEAD/LICENSE) and [NOTICE](https://github.com/lonniev/optionality-mcp/blob/HEAD/NOTICE).