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by cards.tickermint.api
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All-time highs

all_time_highs
Read-onlyIdempotent

Use this when the user asks which cards in one game are at a record price. Returns cards that set an all-time high market price (since February 2024) in the last 7 days and still hold it, with the date of the high, in USD.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gameNoCard game, one of: pokemon, one-piece, lorcana, riftbound, yugioh, gundam.pokemon
limitNo1 to 200.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / game / description
      Added value: +"Card game, one of: pokemon, one-piece, lorcana, riftbound, yugioh, gundam."
    • addedInput schema / properties / game / enum
      Added value: +[
+  "pokemon",
+  "one-piece",
+  "lorcana",
+  "riftbound",
+  "yugioh",
+  "gundam"
+]
    • addedInput schema / properties / limit / description
      Added value: +"1 to 200."
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover read-only, non-destructive, idempotent, and closed-world behavior. The description adds valuable temporal semantics: the high must have been set since February 2024, within the last 7 days, and still be held, plus output details like the date and USD currency. It does not disclose pagination or rate-limit behavior, but with annotations present the added context is substantial.

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

Conciseness5/5

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

The description is three tight sentences with no wasted words. The usage trigger is front-loaded, followed by the precise return conditions, making it easy for an agent to parse quickly.

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

Completeness5/5

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

Given the annotations, full schema coverage, and the presence of an output schema, the description provides everything an agent needs: when to use it, what it returns, and the non-obvious time-window semantics. It is complete for this tool's 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?

Schema description coverage is 100%, so both parameters are fully documented in the input schema. The description implies a game filter ('one game') but does not add syntax, format, or constraints beyond what the schema already provides for game and limit.

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 states a specific verb/resource: it returns cards that set an all-time high market price, with precise scope conditions (since February 2024, last 7 days, still hold it). It distinguishes the tool from its sibling all_time_lows by the 'all-time high' concept and the record-price framing.

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?

The opening sentence gives a clear trigger: 'Use this when the user asks which cards in one game are at a record price.' It does not explicitly name alternative tools or when not to use this one, so it is clear but lacks exclusions.

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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