Lorcana MCP Playtest Platform

A local platform for AI-vs-AI Disney Lorcana matches. AI players connect over theModel Context Protocol (Streamable HTTP) and play exclusively through JSON tool calls;humans watch a live visual spectator board in the browser.

@lorcana/engine — deterministic TypeScript rules engine + JSON effect DSL(turn structure, inkwell, quest/challenge, all keywords, songs, shift, locations,20-lore win, deck-out, seeded replay).
@lorcana/card-data — normalizes the official bulk dump (lorcana-api.com) into2,487 CardDefinitions and auto-generates DSL scripts for every card(see packages/card-data/dist-data/coverage.json for the translation-tier report).
@lorcana/mcp-server — MCP server (SDK 1.30.0) at /mcp + spectator REST/SSE API.
@lorcana/ui — React spectator app (dark ink theme, live SSE updates, card art).
@lorcana/bots — heuristic MCP client that plays both seats (MCP-compliance proof).

Quickstart

npm install                 # at repo root (requires a filesystem that supports symlinks)
npm run build               # builds all packages (card-data → engine → mcp-server → bots → ui)
npm start                   # serves MCP + API + UI on http://localhost:8787

Run an AI-vs-AI match between two decklists (dreamborn/inktable text format):

npx tsx packages/bots/src/run-match.ts \
  --server http://localhost:8787 \
  --deckA "$(cat data/acceptance/deck-emerald-sapphire.txt)" \
  --deckB "$(cat data/acceptance/deck-amber-emerald.txt)" \
  --games 4 --seed 42 --verbose

Then open the printed spectatorUrl (or just http://localhost:8787) to watch live.

Connecting your own AI (MCP)

Point any MCP client at http://localhost:8787/mcp (Streamable HTTP transport).Typical session:

lorcana_search_cards / lorcana_get_card — explore the card pool (real text + stats).
lorcana_validate_deck → lorcana_import_deck — register a 60-card deck(4 Card Name - Subtitle text format; ≤2 inks, ≤4 copies).
lorcana_create_match — returns matchId + one seat token per player.
Loop: lorcana_get_state (fog-of-war view for your seat) →lorcana_get_legal_actions (fully enumerated, ready-to-submit) →lorcana_play_action. Match ends at 20 lore / deck-out / lorcana_concede.

All tool errors use { ok:false, error:{ code, message } }.State is persisted after every action (data/matches/*.json) — the server resumesmatches across restarts.

Card scripts & the effect DSL

Cards contain no code. Every card has a JSON script (dist-data/scripts.json) oftriggers / activated abilities / continuous effects over a small effect vocabulary(DRAW, DEAL_DAMAGE, BANISH, ADD_MODIFIER, CHOICE, FOR_EACH, IF, …) with selector-basedtargeting. Scripts were auto-generated from printed card text:

full — every ability sentence translated (1,195 cards)
partial — keywords + some abilities (428)
vanilla — stats/keywords only (864; they still play correctly as stat cards)

To regenerate: npm run build-data -w @lorcana/card-data. To hand-improve a card,edit its entry in scripts.json following packages/engine/src/effects/dsl.ts.

Known rules limitations

~32% of ability sentences are not yet translated (see coverage.json histogram);affected cards play with keywords/stats only. Sets 10–12 are ~72% sentence-matched;set 13 is not in the bulk dump yet.
Boost / put-under / cost reduction are modeled in the engine; remaining exotictriggers still map to the closest available trigger, and some unique effects(discard→under, play-from-under, deck-ordering) remain documented no-ops.
Core format deck construction only (≤2 inks). Illumineer's Quest cards excluded.
Bulk playtest datapoints: run-match.ts --metrics out.jsonl writes one JSON lineper game (winner, turns, lore, eventCounts).

See packages/*/README.md for per-package contracts and deviation logs. SPEC.md isthe architecture source of truth.

Lorcana MCP Playtest Platform