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DanielTomaro13

sportsdata-mcp

nbl_team_stats

Retrieve team statistics for an NBL season, including totals and per-game averages for assists, rebounds, blocks, steals, turnovers, and shooting percentages.

Instructions

Team statistics for a season — totals and per-game averages across assists, rebounds (offensive/defensive), blocks, steals, turnovers, and shooting (field goals / three-pointers / free throws made-attempted-percentage).

Returns: {type, count, data:[{team, assists, assists_average, defensive_rebounds, blocks, steals, turnovers, field_goals_made, field_goals_attempted, field_goals_percentage, three_pointers_percentage}]}

Example: Team stats, current season

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
seasonTypeNoregular
Behavior2/5

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

No annotations provided, and the description fails to disclose behavioral traits such as data freshness, authentication, rate limits, or side effects. It only describes the output structure, leaving gaps in understanding the tool's behavior.

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?

Description is concise, listing key stats and output format in a single paragraph with an example. However, the return structure is embedded in prose, which could be more structured for readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description reasonably details the return fields. However, it is incomplete regarding parameter meanings and usage context, which is necessary for a tool with 0% schema coverage and no annotations.

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

Parameters2/5

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

Schema description coverage is 0%, yet the description does not elaborate on the two parameters (year, seasonType). It implies a season context but does not explain year format, seasonType allowed values, or how they affect results. This is a significant omission.

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?

Clearly states it returns team statistics for a season with totals and per-game averages, listing specific stats. Distinguishes from sibling tools like nbl_player_stats by focusing on team-level data. However, does not explicitly mention the league (NBL) in description, but the tool name clarifies this.

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 on when to use this tool vs alternatives like nbl_stat_leaders or nbl_player_stats. The example 'Team stats, current season' is vague and provides no context for appropriate usage or prerequisites.

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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