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Optionality — AI-judged options trading drill, Tollbooth-monetized MCP server
About
Optionality — AI-judged options trading drill, Tollbooth-monetized MCP server
Remote endpoints: streamable-http: https://optionality-mcp.fastmcp.app/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-lonniev-optionality-mcp": {
"url": "https://optionality-mcp.fastmcp.app/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
optionality-mcp
MCP server and React drill UI for an AI-judged options trading practice game. Built on FastMCP.
Optionality is an instance of a Tollbooth-DPYC™ 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
- 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.
- 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.
- 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
Optional Nsec Escrow
A patron on a device without a NIP-07 signer (an iPad, say) can opt in to let Optionality hold a game-scoped nsec and sign Nostr DMs for them. escrow_nsec deposits a freshly generated nsec (it must derive to the patron's npub), encrypted at rest and bound to that npub; send_patron_dm signs and publishes a DM as the patron with it; get_escrow_status says whether one is held; withdraw_nsec returns the nsec once and deletes it, after which the patron keeps the key in their own Nostr client. Self-custody with a NIP-07 signer needs none of this.
Repo Layout
optionality-mcp/
├── server.py # FastMCP server (Python) → Horizon
├── tools/ # dealer, judge, journal, leaderboard, profile, options chain, nsec escrow
├── db/ # Neon persistence (journal, leaderboard, patrons, usage, migrations)
├── llm.py # server-side LLM client (operator's vaulted llm_api_key)
├── prompts.py # dealer / judge / clue-desk personas
├── modal_app.py # detached runner for the LLM drills (Modal)
└── frontend/ # React 19 + 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. When the operator has delivered Modal credentials, those jobs run detached on Modal (modal_app.py) instead of on the MCP host.
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 | Python SDK — vault, auth, pricing, Lightning, Nostr identity |
| dpyc-community | Governance registry: membership, advisories, threat model |
| dpyc-oracle | Community concierge (free onboarding + member lookup) |
| tollbooth-authority | Certification backbone (Schnorr-signed certificates) |
| tollbooth-sample | Sample Operator (canonical template) |
| tollbooth-pricing-studio | iOS pricing-model editor / operator console |
| cypher-mcp | Monetized graph answers: named Cypher templates over Neo4j/AuraDB |
| schwab-mcp | Charles Schwab brokerage data |
| thebrain-mcp | TheBrain personal knowledge graph |
| excalibur-mcp | X/Twitter posting |
| taxsort-mcp | Tax classification + Cloudflare Pages UI |
| optionality-mcp | Options-trading practice game (this repo) |
| goodearth-mcp | Region-scoped climate analytics for small farms |
| beesknees-mcp | The Bee's Knees: a multiplayer race to the queen |
| roastify-mcp | Roastify coffee catalog and packaging artwork |
| ChartRemotely-mcp | Puts a security on a desktop chart remotely |
| tollbooth-web | Shared browser SDK for operator front ends |
| tollbooth-oauth2-collector | OAuth2 callback handler (advocate service) |
| tollbooth-shortlinks | URL shortener utility |
License
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