mtg-search

Semantic search over every Magic: The Gathering card, as an MCP server for deck-building agents.

An LLM building a Commander deck from memory gets the rules wrong (off-colour cards, cards that are not legal, cards already in the list) and keeps reaching for the same popular staples. mtg-search gives the agent the official rules text of all 34,646 cards, a search engine over it, and the filters to check its picks.

Write-up: Does a search engine help a deck-building agent?

Install

One line for Claude Code, with uv installed. It downloads the card index once (about 1.9 GB) and registers the server:

uvx mtg-search-mcp --download && claude mcp add --scope user mtg-search -- uvx mtg-search-mcp

Any other MCP client runs uvx mtg-search-mcp over stdio:

{
  "mcpServers": {
    "mtg-search": { "command": "uvx", "args": ["mtg-search-mcp"] }
  }
}

The server uses about 2.7 GB of RAM and keeps the index in ~/.cache/mtg-search (set MTG_SEARCH_HOME to move it). To update, run uvx mtg-search-mcp@latest --download; a new card snapshot that has not been downloaded yet is fetched in the background while the previous one keeps serving.

With Docker instead (no Python needed; the image already contains the index):

docker pull ghcr.io/kasperg3/mtg-search && claude mcp add --scope user mtg-search -- docker run -i --rm ghcr.io/kasperg3/mtg-search

Then ask for cards the way a player would: "upgrade my Auntie Ool precon", "cheap ways to protect my commander in Selesnya", "what else works like Smothering Tithe?", or point it at your own list: "swap ten cards in https://moxfield.com/decks/…"

Tools

What the agent gets back, for search_cards(queries=["Deals 3 damage to any target."], color_identity="R", types=["instant"], mana_value_max=1, format="commander", limit=3):

search: "Deals 3 damage to any target." · filters: color_identity=R, format=commander, types=['instant'], mana_value_max=1 · 3 shown of 152 matching cards
1. Lightning Bolt — {R} (MV 1) · Red · Instant · uncommon
   Lightning Bolt deals 3 damage to any target.
   roles: burn-any
   legal: commander ✓
2. Collateral Damage — {R} (MV 1) · Red · Instant · common
   As an additional cost to cast this spell, sacrifice a creature.
   Collateral Damage deals 3 damage to any target.
   roles: bombard, sacrifice-outlet-creature, burn-any, lightning-bolt-redux
   legal: commander ✓
3. Shard Volley — {R} (MV 1) · Red · Instant · common
   ...

Results are ordered by text relevance only. Prices, EDHREC rank and other popularity signals are not in the image at all, so the agent picks cards for what they do, not for how often they are played.

How well it works

All runs use Claude Sonnet 5.5 in Claude Code, and cards were graded blind by a separate Claude model. Details, intervals and every caveat: write-up and eval/experiments/.

Search vs grep, same agent, same minimal instructions (113 tasks; relevance over the 80 deck-need and card-attribute tasks):

Real decks. Asked to add 20 cards to each of 14 Commander precons released after 1 November 2025, the agent's adds fit the deck better with mtg-search than from memory: deck-fit score 0.45 → 0.65 (0 to 1), better on 13 of 14 decks, p = 0.002.

The engine. On 46 rules-text queries, nDCG@10 is 70.7 for lightonai/mLateOn (late interaction), 61.1 for BM25 and 36.7 for a dense embedding (EXP-001).

How it works

• Data: Scryfall's Oracle cards (one entry per card, current official wording), Tagger function tags, the official rulings and MTGJSON's official precon decklists, refreshed weekly by scripts/refresh.py.
• Search: exact late interaction with mLateOn. Queries go through the model's fp32 ONNX encoder; every card is scored by exact MaxSim against its precomputed token vectors, about 100–150 ms per query on a 6-core desktop CPU. Nothing is approximated, so the shipped server ranks exactly like the benchmarks' reference (docker/README.md).
• Server: Python, MCP over stdio, one Docker image.

Datasets

All on Hugging Face; cite a tagged revision (BibTeX).

Repository

src/mtg_search/   the MCP server, ingest, filters and the mLateOn search
docker/           the image (Dockerfile, build script, smoke test)
eval/             search benchmark, agent tasks and harness, experiment write-ups (eval/experiments/)
bench/precon/     the Commander precon benchmark: data build, agent harness, scorer, deck-fit judge
scripts/          weekly index refresh, dataset publishing, result sync
jobs/             the GPU encoding job for the card embeddings
docs/             dataset cards, findings page, plan and design notes
tests/            unit tests and the Docker integration test

Development

uv sync
uv run pytest                    # unit tests
docker/build.sh                  # build the image from the HF dataset
uv run pytest -m docker          # run the built image over stdio
python docker/mcp_smoke.py       # sample calls with output and latency

Run the server from a checkout with uv run mtg-search-mcp; on first run it downloads the index into ~/.cache/mtg-search, like the published package.

Releases

Every version bump on main is published automatically (publish.yml): tests, then PyPI, a vX.Y.Z GitHub release and the matching Docker image. Each release pins one card snapshot; when the index refresh tags a new one, scripts/bump_index.py points the package at it and bumps the patch version, which releases it.

Data and terms

Card names and rules text are © Wizards of the Coast, used under the Wizards of the Coast Fan Content Policy. This is unofficial Fan Content, not approved or endorsed by Wizards. Card data comes from Scryfall, function tags from Scryfall Tagger, precon decklists from MTGJSON and precon add/cut rates from EDHREC. This project is not affiliated with any of them.

License

The code is MIT licensed (LICENSE). The card data and the benchmarks keep their sources' terms, described above.