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MCP server for searching, exploring, and aggregating Disney Lorcana cards.
MCP server for searching, exploring, and aggregating Disney Lorcana cards.
Valid MCP server (0 strong, 3 medium validity signals). 4 known CVEs in dependencies (1 critical, 1 high severity) Imported from the Official MCP Registry.
12 files analyzed · 4 issues found
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From the project's GitHub README.
An MCP server for searching and aggregating Disney Lorcana cards.
On startup, the server fetches all cards from lorcanajson.org via a GET request to https://lorcanajson.org/files/current/en/allCards.json.
Cards are kept in-memory as a Python list for fast filtering. With ~2,700 cards this is lightweight and requires no external database. A local JSON file cache (LORCANA_CACHE_PATH, default cards.json) lets the server skip the API fetch on subsequent startups.
Startup data loading is controlled by:
LORCANA_REFRESH_ON_STARTUP:
true: always fetch from API and repopulate storagefalse: use existing cache if availableLORCANA_SKIP_IF_DB_EXISTS:
true (default): skip API fetch if the cache file already contains cardsfalse: fetch and repopulateThe server is published to GHCR and the MCP Registry. Pull and run it directly:
docker pull ghcr.io/danielenricocahall/lorcana-mcp:latest
docker run --rm -i ghcr.io/danielenricocahall/lorcana-mcp:latest
To persist the card cache across container restarts, mount a volume:
docker run --rm -i \
-e LORCANA_CACHE_PATH=/data/cards.json \
-e LORCANA_SKIP_IF_DB_EXISTS=true \
-v lorcana_mcp_data:/data \
ghcr.io/danielenricocahall/lorcana-mcp:latest
uv run python main.py
docker build -t lorcana-mcp:latest .
docker run --rm -i lorcana-mcp:latest
docker compose build
docker compose run --rm -T lorcana-mcp
Notes:
LORCANA_API (default: https://lorcanajson.org/files/current/en/allCards.json)LORCANA_CACHE_PATH (default: cards.json) — local file for caching fetched cardsLORCANA_HTTP_TIMEOUT_SECONDS (default: 60)LORCANA_REFRESH_ON_STARTUP (false default)LORCANA_SKIP_IF_DB_EXISTS (true default){
"mcpServers": {
"lorcana": {
"command": "uv",
"args": ["run", "python", "/absolute/path/to/lorcana-mcp/main.py"]
}
}
}
{
"mcpServers": {
"lorcana": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"ghcr.io/danielenricocahall/lorcana-mcp:latest"
]
}
}
}
{
"mcpServers": {
"lorcana": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"lorcana-mcp:latest"
]
}
}
}
{
"mcpServers": {
"lorcana": {
"command": "docker",
"args": ["compose", "run", "--rm", "-T", "lorcana-mcp"]
}
}
}
claude mcp add --scope user \
-- lorcana docker run --rm -i \
ghcr.io/danielenricocahall/lorcana-mcp:latest
claude mcp add --scope user \
-- lorcana docker run --rm -i lorcana-mcp:latest
Once connected to an MCP client, you can ask natural language questions like:
Card lookup
Deck building
Keyword & ability search
Stats & aggregations
Cross-filter queries
Note: Keyword searches like
Evasive,Singer 5, orRecklessmatch against thefull_textfield and work reliably since keywords appear verbatim in card text.
search_cards — filter and retrieve card objectscount_cards — count cards matching a filter without returning full objectsget_card_by_id — fetch a single card by its IDaggregate_cards — count cards grouped by any fieldink_curve_stats — card counts by ink costtop_traits — most common traits across all cardscolor_distribution — card count per colorrarity_breakdown — card count per rarityset_distribution — card count per setserver_status — startup metadata (card count, config)Be the first to review this server!
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