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dburge86

kenpom-mcp

by dburge86

get_player_stats

Rank players by a chosen KenPom statistic (e.g., eFG, ORtg) with optional season and conference filters to find leaders.

Instructions

Get player leaders by statistical metric.

Args: metric: Stat to rank players by. Options: 'ORtg', 'Min', 'eFG', 'Poss', 'Shots', 'OR', 'DR', 'TO', 'ARate', 'Blk', 'FTRate', 'Stl', 'TS', 'FC40', 'FD40', '2P', '3P', 'FT'. Default: 'eFG'. season: Optional season year (e.g., "2024"). Defaults to current season. Earliest available: 2004. conference: Optional conference filter (e.g., 'ACC', 'B10', 'SEC').

Returns: JSON array of player stats ranked by the specified metric.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricNoeFG
seasonNo
conferenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description discloses the return format (JSON array), defaults for metric and season, and the earliest available season (2004). However, it does not clarify sort order (ascending/descending) or player qualification thresholds, which are important for a leaderboard tool. With no annotations, these gaps are notable.

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 uses a compact docstring format with a clear purpose sentence, then Args/Returns sections. The metric list is lengthy but necessary; there is no redundant or filler content.

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?

The description covers purpose, all parameters, return type, and an important constraint (earliest season). An output schema exists, so return details need not be fully spelled out. Minor omissions like qualification criteria and sort direction prevent a perfect score.

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?

The schema provides zero description coverage, so this description carries the full burden. It enumerates all metric options, defines season default and earliest year, and gives conference examples – adding substantial meaning beyond the raw type/default fields in the 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 opens with 'Get player leaders by statistical metric,' which is a specific verb+resource statement. It clearly differentiates this tool from sibling tools like get_team_stats or get_ratings by focusing on player leaderboards rather than team-level metrics.

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?

No explicit guidance is given for when to use this tool versus alternatives like get_efficiency or get_ratings. The description implies usage through the metric list but does not mention exclusions or alternative tools for similar stats.

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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