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AsafShai

Fantasy NBA Israel League MCP

by AsafShai

getAverageStats

Retrieve per-game statistical averages for all teams in the Fantasy NBA Israel League, including points, rebounds, assists, and shooting percentages, to analyze actual team performance.

Instructions

Get the average statistics (actual performance numbers) for all teams from the API.

IMPORTANT: This returns ACTUAL PERFORMANCE STATS, NOT ranking points!
- This is different from getAveragesLeagueRankings() which returns ranking points
- Use this endpoint to see actual per-game averages (e.g., 25.3 assists per game)
- Use getAveragesLeagueRankings() to see rotisserie ranking points (e.g., 12 points earned)

Args:
    use_normalized: If True, returns normalized data (0-1 scale) for comparison.
                   If False, returns raw statistical values (e.g., 45.6% FG, 12.3 AST).
                   Default is False.

Returns:
    A list of teams with their actual statistical averages per game.
    Each item in the list is a dictionary with the following structure:
    {
        "team": {
            "team_id": <team_id>,
            "team_name": <team_name>
        },
        "stats": {
            "FG%": <field_goal_percentage_as_decimal>,
            "FT%": <free_throw_percentage_as_decimal>,
            "3PM": <three_pointers_made_per_game>,
            "AST": <assists_per_game>,
            "REB": <rebounds_per_game>,
            "STL": <steals_per_game>,
            "BLK": <blocks_per_game>,
            "PTS": <points_per_game>,
            "GP": <games_played>
        }
    }
    
NOTES:
- Percentages are decimals (0.456 = 45.6%)
- All counting stats (3PM, AST, REB, STL, BLK, PTS) are per-game averages
- When use_normalized=True, all values are scaled 0-1 for heatmap visualization
- GP (games played) is a total count, not an average

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
use_normalizedNo
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It effectively discloses behavioral traits: it describes the return format (list of teams with dictionaries), explains data types (percentages as decimals, counting stats as per-game averages, GP as total count), and details the effect of the use_normalized parameter (scales values 0-1 for heatmap visualization). It doesn't mention rate limits or authentication needs, but covers core functionality well.

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?

The description is appropriately sized and front-loaded, starting with the core purpose and key distinction from siblings. Each section (Args, Returns, NOTES) adds value without redundancy. It could be slightly more concise in the Returns section, but overall, sentences earn their place by clarifying critical details.

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 complexity (statistical data with normalization options) and lack of annotations or output schema, the description is highly complete. It covers the tool's purpose, usage guidelines, parameter semantics, return structure, and data formatting notes, providing all necessary context for an AI agent to invoke it correctly without relying on external documentation.

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 and only one parameter, the description fully compensates by adding rich semantic details. It explains the use_normalized parameter's purpose ('returns normalized data (0-1 scale) for comparison' vs. 'raw statistical values'), provides examples (e.g., '45.6% FG, 12.3 AST'), and specifies the default value (False), going well beyond the minimal 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 clearly states the tool 'gets average statistics (actual performance numbers) for all teams from the API,' specifying both the verb ('get') and resource ('average statistics for all teams'). It explicitly distinguishes this from the sibling tool getAveragesLeagueRankings by emphasizing it returns 'ACTUAL PERFORMANCE STATS, NOT ranking points,' providing clear differentiation.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool versus alternatives. It states: 'Use this endpoint to see actual per-game averages (e.g., 25.3 assists per game)' and 'Use getAveragesLeagueRankings() to see rotisserie ranking points (e.g., 12 points earned),' clearly defining the context and naming the alternative tool.

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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