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AsafShai

Fantasy NBA Israel League MCP

by AsafShai

getTeamDetails

Retrieve comprehensive team statistics for Fantasy NBA Israel League, including raw averages, rotisserie ranking points, category ranks, and player roster details to analyze team performance.

Instructions

Get comprehensive details for a specific team from the API.

This endpoint combines multiple data types for a single team:
1. Raw statistical averages (actual performance numbers)
2. Ranking points (rotisserie scoring system)
3. Category ranks (position in each category, 1=best)
4. Player roster with individual stats

IMPORTANT - Understanding the Data Sections:

"raw_averages" = Actual statistical performance (e.g., 45.6% FG, 12.3 assists per game)
"ranking_stats" = Rotisserie points earned in each category (see explanation below)
"category_ranks" = Ordinal position in each category (1=1st place, 2=2nd place, etc.)
"shot_chart" = Raw totals for field goals and free throws (not averages)

RANKING STATS EXPLANATION (Same as getAveragesLeagueRankings):
This is a ROTISSERIE (ROTO) fantasy league. Teams earn ranking points in 8 categories.

CRITICAL: Do NOT confuse "ranking points" with "category ranks"!
- ranking_stats values (e.g., ast: 12.0) = POINTS earned (higher is better)
- category_ranks values (e.g., AST: 1) = position/place (lower is better, 1 = first)

HOW RANKING POINTS WORK:
- In each category, teams are ranked 1st to Nth (where N = number of teams)
- Best team in a category gets N points, second-best gets N-1, worst gets 1
- Example in 12-team league: 1st place = 12 pts, 2nd = 11 pts, ..., 12th = 1 pt
- total_points = sum of points from all 8 categories
- Overall "rank" is determined by total_points (highest total = rank 1)

Args:
    team_id: The ID of the team to get details for. Use getTeams() to see all team IDs.

Returns:
    A dictionary containing comprehensive team information: {
        "team": {
            "team_id": <team_id>,
            "team_name": <team_name>
        },
        "espn_url": <espn_team_page_url_string>,
        
        "shot_chart": {
            "team": {"team_id": <id>, "team_name": <name>},
            "fgm": <total_field_goals_made>,
            "fga": <total_field_goals_attempted>,
            "fg_percentage": <calculated_field_goal_percentage_as_decimal>,
            "ftm": <total_free_throws_made>,
            "fta": <total_free_throws_attempted>,
            "ft_percentage": <calculated_free_throw_percentage_as_decimal>,
            "gp": <games_played>
        },
        
        "raw_averages": {
            "fg_percentage": <average_field_goal_percentage_as_decimal>,
            "ft_percentage": <average_free_throw_percentage_as_decimal>,
            "three_pm": <average_three_pointers_made_per_game>,
            "ast": <average_assists_per_game>,
            "reb": <average_rebounds_per_game>,
            "stl": <average_steals_per_game>,
            "blk": <average_blocks_per_game>,
            "pts": <average_points_per_game>,
            "gp": <games_played>,
            "team": {"team_id": <id>, "team_name": <name>}
        },
        
        "ranking_stats": {
            "team": {"team_id": <id>, "team_name": <name>},
            "fg_percentage": <ranking_points_earned_in_fg_percentage>,
            "ft_percentage": <ranking_points_earned_in_ft_percentage>,
            "three_pm": <ranking_points_earned_in_three_pointers>,
            "ast": <ranking_points_earned_in_assists>,
            "reb": <ranking_points_earned_in_rebounds>,
            "stl": <ranking_points_earned_in_steals>,
            "blk": <ranking_points_earned_in_blocks>,
            "pts": <ranking_points_earned_in_points>,
            "gp": <games_played_not_ranked>,
            "total_points": <sum_of_all_8_category_ranking_points>,
            "rank": <overall_standing_1_is_first_place>
        },
        
        "category_ranks": {
            "FG%": <ranking_points_earned_in_fg_percentage>,
            "FT%": <ranking_points_earned_in_ft_percentage>,
            "3PM": <ranking_points_earned_in_three_pointers>,
            "AST": <ranking_points_earned_in_assists>,
            "REB": <ranking_points_earned_in_rebounds>,
            "STL": <ranking_points_earned_in_steals>,
            "BLK": <ranking_points_earned_in_blocks>,
            "PTS": <ranking_points_earned_in_points>
        },
        
        "players": [
            {
                "player_name": <player_full_name_string>,
                "pro_team": <nba_team_abbreviation_string>,
                "positions": <list_of_eligible_positions>,
                "stats": {
                    "pts": <average_points_per_game>,
                    "reb": <average_rebounds_per_game>,
                    "ast": <average_assists_per_game>,
                    "stl": <average_steals_per_game>,
                    "blk": <average_blocks_per_game>,
                    "fgm": <average_field_goals_made_per_game>,
                    "fga": <average_field_goals_attempted_per_game>,
                    "ftm": <average_free_throws_made_per_game>,
                    "fta": <average_free_throws_attempted_per_game>,
                    "fg_percentage": <field_goal_percentage_as_decimal>,
                    "ft_percentage": <free_throw_percentage_as_decimal>,
                    "three_pm": <average_three_pointers_made_per_game>,
                    "minutes": <average_minutes_per_game>,
                    "gp": <total_games_played>
                },
                "team_id": <fantasy_team_id>
            }
        ]
    }
    
EXAMPLE - Understanding the Different Data Types:
If a team shows:
- raw_averages.ast: 25.3 → Team averages 25.3 assists per game (actual performance)
- ranking_stats.ast: 12.0 → Team earned 12 ranking points in assists (1st place in 12-team league)
- category_ranks.AST: 1 → Team is ranked 1st in assists category

NOTES:
- Higher ranking_stats values = more points earned = better
- Lower category_ranks values = better position (1 is first place)
- raw_averages are the actual statistical performance
- GP (games played) is informational only, not used in ranking calculations
- When referring to steals in Hebrew, use חטיפות (not גניבות)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idYes
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 of behavioral disclosure. It thoroughly explains the data structure, including key distinctions (e.g., ranking points vs. category ranks, raw totals vs. averages), and provides an example to illustrate differences. However, it lacks explicit information about rate limits, authentication requirements, or error handling, which would be needed for a perfect score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (e.g., 'IMPORTANT', 'HOW RANKING POINTS WORK', 'EXAMPLE'), but it is excessively long (over 500 words) with redundant explanations (e.g., repeating ranking stats details). Some content, like the Hebrew translation note, is unnecessary for tool selection. While informative, it could be more concise without losing critical information.

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 of the tool (returns multiple nested data types), no annotations, and no output schema, the description provides exceptional completeness. It details all return sections with field explanations, includes an example to clarify data interpretation, and explains domain-specific concepts (rotisserie scoring). This fully compensates for the lack of structured metadata.

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 has 0% description coverage (parameter 'team_id' has no description in schema), so the description must fully compensate. It does so by explaining: 'team_id: The ID of the team to get details for. Use getTeams() to see all team IDs.' This provides clear semantics, source guidance, and practical usage instructions beyond the bare schema type.

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's purpose: 'Get comprehensive details for a specific team from the API.' It specifies the verb ('Get'), resource ('team details'), and scope ('comprehensive'), distinguishing it from siblings like getTeams (list teams) or getAverageStats (league-wide averages). The detailed breakdown of data sections further clarifies what 'comprehensive' entails.

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: 'Use getTeams() to see all team IDs' to obtain the required team_id parameter. It distinguishes this tool from siblings by explaining that it combines multiple data types (raw averages, ranking stats, etc.) for a single team, unlike getAveragesLeagueRankings (league-wide rankings) or getAverageStats (league averages). The 'IMPORTANT' and 'CRITICAL' sections further clarify usage context.

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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