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All-time lows

all_time_lows
Read-onlyIdempotent

Use this when the user asks which cards in one game are at their lowest price or have crashed. Returns cards worth $5+ sitting at their all-time low market price after falling at least 50% from their high, with the high and the percent below it, 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 declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds real value beyond them by disclosing the hardcoded filter thresholds and what is included in the result set (cards $5+ that have fallen ≥50%). It does not mention pagination or result ordering, which keeps it short of a 5.

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?

Two sentences, no waste: the usage trigger comes first and the return-set definition second. Every clause earns its place, and the front-loading is correct.

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?

With an output schema present, the description need not explain return shape, yet it still defines the qualifying set precisely. Together with annotations covering safety and a fully documented schema, an agent has everything needed to select and call it correctly.

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%, with game enumerated and limit defaulted and bounded (1–200), so the schema already carries full parameter meaning. The description adds nothing about how game or limit affect output, which is the expected baseline 3 when the schema does the heavy lifting.

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 and resource — surfacing cards at their all-time low — and pins the exact scope with quantitative filters ($5+ worth, ≥50% below the high). It is clearly distinguishable from the sibling all_time_highs, which would be the opposite screen.

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 gives an explicit trigger: 'when the user asks which cards in one game are at their lowest price or have crashed.' That is a clear usage context, but it never names all_time_highs or movers as the alternative to avoid, so the exclusion half is left to inference.

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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