lorcana-mcp

deck_stats

Compute Lorcana deck stats—ink curve, color split, type, keyword and subtype counts, plus per-card keyword tags—to analyze deck composition and answer archetype questions.

Instructions

Compute summary stats for a Lorcana deck: ink curve (copies at each cost), color split (copies per color — dual-ink contributes to BOTH buckets, so the sum may exceed total_cards), inkable/uninkable counts, type breakdown (Character/Action/Item/Song/Location), keyword_counts (copies per keyword — Evasive, Ward, Bodyguard, Shift, Singer, ...), subtype_counts (copies per subtype — Floodborn, Hero, Puppy, ...), and card_keywords (per-card keyword tags, so archetype questions like 'how many Evasive bodies does this run?' are answerable without a follow-up search_cards call). Each entry is {name: str, count: int} where name is the card's full_name. Names that don't resolve are listed under unresolved and excluded from the per-card stats (but still counted in total_cards).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "deck": "<deck>"
    },
    "name": "deck_stats"
  }
}

Input Schema

Name	Required	Description	Default
`deck`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.2.0Jul 7, 2026

TDQS

A4.3/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and succeeds: it discloses dual-ink counting behavior (sum may exceed total_cards), the handling of unresolved names (listed separately, excluded from per-card stats, still counted in total_cards), and the inclusion of per-card keyword tags.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a dense paragraph, but every clause adds specific information about a stat or edge case. It is longer than ideal, but the length is justified by the variety of stats. Slight chunking into bullets would improve scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema exists to cover return values, and the description thoroughly covers behavioral edge cases and stat semantics. The only minor gap is the implicit, not fully explicit, mapping from the deck parameter to the described entry format; otherwise it is complete for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The `deck` parameter has zero schema coverage, but the description partially compensates by indicating each entry is `{name, count}` where name is the card's full_name and explaining how unresolved names are handled. It could be more explicit about the deck input format (e.g., that deck is the array of these entries), but enough meaning is added beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with an explicit verb and object ('Compute summary stats for a Lorcana deck') and enumerates all stat categories (ink curve, color split, inkable counts, type breakdown, keyword/subtype counts, card_keywords). This makes it clearly distinguishable from sibling search/count/aggregate tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states a concrete benefit over an alternative ('answerable without a follow-up search_cards call'), which implies when to use this tool instead of searching. It does not explicitly enumerate when not to use it, but the scope (deck-level summarization) is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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