Optionality MCP — AI-judged options trading drill. Tollbooth DPYC monetized. FastMCP + React/Vite.
- ✓Open-source license (Apache-2.0)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Documented (README)
git clone https://github.com/lonniev/optionality-mcpTools overview
# 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](LICENSE) and [NOTICE](NOTICE).
What people ask about optionality-mcp
What is lonniev/optionality-mcp?
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lonniev/optionality-mcp is tools for the Claude AI ecosystem. Optionality MCP — AI-judged options trading drill. Tollbooth DPYC monetized. FastMCP + React/Vite. It has 0 GitHub stars and its last recorded update is dated 2026-08-22.
How do I install optionality-mcp?
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You can install optionality-mcp by cloning the repository (https://github.com/lonniev/optionality-mcp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is lonniev/optionality-mcp safe to use?
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Our security agent has analyzed lonniev/optionality-mcp and assigned a Trust Score of 87/100 (tier: Trusted). See the full breakdown of passed checks and flags on this page.
Who maintains lonniev/optionality-mcp?
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lonniev/optionality-mcp is maintained by lonniev. The last recorded GitHub activity is dated 2026-08-22, with 2 open issues.
Are there alternatives to optionality-mcp?
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Yes. On ClaudeWave you can browse similar tools at /categories/tools, sorted by popularity or recent activity.
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