Sports Trading Card Agent

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trending_players

Identify NBA players with breakout performances to find trading cards likely increasing in value. Scans a curated watchlist of young stars and ascending players for investment opportunities.

Instructions

Get a list of NBA players with breakout performances whose trading cards are likely rising in value. Scans a curated watchlist of young stars and ascending players.

Args: limit: Number of trending players to return (1-20). Default: 10

Returns: Ranked list of trending players with their stats, trend score, breakout reasons, and card buying tips.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "trending_players"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Mar 24, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It successfully discloses behavioral scope ('Scans a curated watchlist' clarifies this is not exhaustive) and output structure ('Returns' section details trend scores and buying tips). It misses operational details like data freshness or caching behavior, but covers the essential behavioral context.

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 optimally structured with a clear purpose statement followed by Args and Returns sections. Every sentence earns its place: the first establishes function, the second explains methodology (curated scan), and the structured sections document the single parameter and expected output without redundancy.

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 low complexity (1 optional parameter) and the presence of an output schema, the description provides excellent completeness. It compensates for poor schema documentation via the Args section and voluntarily documents the return structure, leaving no critical gaps for an AI agent to invoke this tool correctly.

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

Parameters5/5

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

Despite 0% schema description coverage (the limit property lacks a description field), the Args section fully compensates by documenting the parameter's purpose ('Number of trending players to return'), constraints ('1-20'), and default value ('10'), providing complete semantic meaning beyond the raw 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 uses a specific verb ('Get a list') and resource ('NBA players with breakout performances whose trading cards are likely rising in value'), and explicitly differentiates from generic player lookups via the 'curated watchlist of young stars' and 'breakout performances' scope, distinguishing it from sibling tools like player_stats_lookup or card_price_lookup.

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

Usage Guidelines3/5

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

The description implies usage context through 'breakout performances' and 'rising in value,' suggesting when to use this tool (identifying trending investments), but lacks explicit guidance on when to prefer alternatives like player_stats_lookup for general statistics or card_market_analysis for broader market trends.

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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