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rjexile

Sports Trading Card Agent

by rjexile

player_stats_lookup

Retrieve player statistics for NBA, NFL, or MLB athletes and analyze trading card market insights based on their performance data.

Instructions

Look up player stats for NBA, NFL, or MLB and get card market insights based on their performance.

Args: player_name: Player name, e.g. "LeBron James", "Patrick Mahomes", "Shohei Ohtani". Partial names work. sport: "nba", "nfl", or "mlb". Default: "nba" season: Season year (e.g. 2025). Default: 2025

Returns: Player bio, current season stats, and card market insight based on their performance level.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
player_nameYes
sportNonba
seasonNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It successfully discloses that 'partial names work' (critical behavioral trait) and outlines return contents ('Player bio, current season stats, and card market insight'). However, it lacks information on error handling, rate limits, or matching behavior when multiple players match a partial name.

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?

Well-structured with front-loaded purpose statement followed by Args and Returns sections. Every sentence serves a documentation need. Slightly verbose due to docstring-style formatting, but highly readable and appropriately sized for the parameter complexity.

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?

Given the existence of an output schema (not shown but indicated), the description appropriately summarizes return values without overspecifying. Parameters are thoroughly documented. Minor gap: sibling tool relationships remain unaddressed, which would be helpful given the nine siblings including overlapping sporting domains.

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?

With 0% schema description coverage, the description fully compensates by documenting all three parameters with examples (e.g., 'LeBron James'), enumerated valid values for 'sport' ('nba', 'nfl', 'mlb'), notes on partial name matching, and default values. This maximizes clarity for a zero-coverage schema.

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

Purpose4/5

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

Clear verb ('Look up') and resources ('player stats', 'card market insights'), with explicit scope covering NBA, NFL, or MLB. However, it fails to distinguish from siblings like 'mlb_stats_lookup' or 'nfl_stats_lookup'—it doesn't state when to use this multi-sport version versus the sport-specific alternatives.

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

Usage Guidelines2/5

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

No guidance provided on when to select this tool versus 'mlb_stats_lookup', 'nfl_stats_lookup', or the various card-specific siblings. No prerequisites or exclusion criteria mentioned despite the complex sibling landscape.

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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