Midpoint Card Prices

Trending cards (30-day price movers)

trending_cards

Read-onlyIdempotent

Use this when the user asks which cards are rising, hot, spiking, crashing or trending, in one game or across all games. Returns the biggest 30-day gainers (or drops) with the percentage change, measured on the PSA 10 price where the card has one and on the raw price otherwise; moves over 300% are excluded as bad data. Do not use for a single named card or for long-term history.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`game`	No	Restrict to one game or sport; omit for all.
`limit`	No
`direction`	No	"up" = biggest gainers, "down" = biggest drops.	up
`min_market_usd`	No	Ignore cards whose raw price is below this.

Output Schema

TableJSON Schema

Name	Required	Description
`game`	Yes
`cards`	Yes
`count`	Yes
`criteria`	Yes
`direction`	Yes
`window_days`	Yes
`excluded_unstable`	Yes	Candidates dropped because their own 40-day series did not hold together (too few captures, a baseline that swings >2×, a >6× range, or a one-capture jump).

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 19, 2026

addedOutput schema / properties / criteria
```
Added value: +{
+  "type": "string"
+}
```

addedOutput schema / properties / excluded_unstable

Added value: +{
+  "description": "Candidates dropped because their own 40-day series did not hold together (too few captures, a baseline that swings >2×, a >6× range, or a one-capture jump).",
+  "type": "integer"
+}

changedOutput schema / required

Previous value: -[
-  "window_days",
-  "direction",
-  "game",
-  "count",
-  "cards"
-]New value: +[
+  "window_days",
+  "direction",
+  "game",
+  "count",
+  "excluded_unstable",
+  "criteria",
+  "cards"
+]

First observedSep 11, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Adds substantial behavioral detail beyond the readOnly/idempotent annotations: the 30-day window, PSA 10 versus raw price fallback, and the exclusion of moves over 300% as bad data. These are non-obvious quirks an agent must know before trusting the results.

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?

Three sentences, front-loaded with the primary use case, then data-source detail, then exclusions. No filler or redundancy; every sentence adds actionable information.

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?

For a read-only listing tool with optional filters and an output schema, the description covers all essential aspects: when to use, what is returned, how prices are calculated, exclusions, and what not to use it for. Nothing important is missing.

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 high (75%), so the description does not need to re-explain parameters. It does reinforce that game may be omitted for cross-game queries, but it adds little beyond the schema; parameters like limit and direction are already self-documenting.

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?

Description states a specific action ('Returns the biggest 30-day gainers or drops'), names the resource ('trending cards'), and gives clear trigger phrasing ('rising, hot, spiking, crashing or trending'). It also excludes single-card and long-term-history use cases, which distinguishes it from get_card_prices and get_price_history.

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?

Explicitly says 'Use this when the user asks which cards are rising...' and 'Do not use for a single named card or for long-term history,' giving both positive and negative usage signals. It does not explicitly name which sibling tool to use as an alternative, but the intent is clear.

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

All Midpoint Card Prices tools Inspect

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