Skip to main content
Glama
AsafShai

Fantasy NBA Israel League MCP

by AsafShai

getAveragesLeagueRankings

Retrieve average league rankings for Fantasy NBA Israel League teams, displaying roto scoring across 8 categories with total points and standings.

Instructions

Get the average league rankings from the API.

IMPORTANT - SCORING SYSTEM EXPLANATION:
This is a ROTISSERIE (ROTO) fantasy league. Teams earn ranking points in 8 categories.

CRITICAL: Do NOT confuse "ranking points" with "rank position"!
- Category values (fg_percentage, ast, reb, etc.) = POINTS earned (higher is better)
- The "rank" field = actual position/place in standings (1 = first place)

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)

EXAMPLE in a 12-team league:
{
    "team": {"team_name": "Best Team"},
    "ast": 12.0,           // Earned 12 pts (1st place in assists)
    "reb": 11.0,           // Earned 11 pts (2nd place in rebounds)
    "stl": 8.0,            // Earned 8 pts (5th place in steals)
    ...other categories...
    "total_points": 73.0,  // Sum of all 8 category points
    "rank": 1,             // Overall standing: 1st place
    "GP": 55               // Games played (informational only, not ranked)
}

Args:
    order: Sort order for rankings.
           - "desc" = best to worst (top teams first, "from top to bottom", "מלמעלה למטה")
           - "asc" = worst to best (bottom teams first, "from bottom to top", "מלמטה למעלה")
           Default is "desc".

Returns:
    A list of teams with their rankings, total points, and stats per category.
    Each item in the list is a dictionary with the following keys: {
        "team": {
            "team_id": <team_id>,
            "team_name": <team_name>
        },
        "fg_percentage": <ranking_points_for_field_goal_percentage>,
        "ft_percentage": <ranking_points_for_free_throw_percentage>,
        "three_pm": <ranking_points_for_three_pointers_made>,
        "ast": <ranking_points_for_assists>,
        "reb": <ranking_points_for_rebounds>,
        "stl": <ranking_points_for_steals>,
        "blk": <ranking_points_for_blocks>,
        "pts": <ranking_points_for_points>,
        "total_points": <sum_of_all_category_ranking_points>,
        "rank": <overall_position_1_is_first_place>,
        "GP": <games_played_not_ranked>
    }
    
NOTES:
- Higher values in categories = better performance (more ranking points earned)
- "rank" field is opposite: lower number = better (1 is first place)
- GP (games played) is informational only, not used in scoring
- When referring to steals in Hebrew, use חטיפות (not גניבות)
- If you refer to comparing teams, distinguish between rank and total points, so f.e someone can be 1st place 70 total points and another team can be 2nd place 69 total points.
- In that case, the difference is 2 points, not 1 place.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orderNodesc
Behavior5/5

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 excels by detailing the scoring system, clarifying critical distinctions (e.g., ranking points vs. rank position), providing an example output, and noting language-specific considerations (Hebrew terms). This gives the agent a comprehensive understanding of how the tool behaves and what to expect from its results.

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 front-loaded with the core purpose but becomes lengthy due to extensive explanations of the scoring system, example, and notes. While all content is relevant and earns its place, the structure could be more streamlined—some details (e.g., Hebrew translations, specific comparison examples) might be overly verbose for a tool description, reducing overall conciseness.

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 rotisserie scoring system, no annotations, and no output schema, the description provides exceptional completeness. It thoroughly explains the data model, scoring logic, parameter usage, and return structure, ensuring the agent has all necessary context to invoke the tool correctly and interpret results 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?

The input schema has 0% description coverage and only one parameter ('order') with minimal schema details. The description compensates fully by explaining the parameter's semantics: it defines the sort order options ('desc' for best to worst, 'asc' for worst to best), provides default behavior, and includes multilingual clarifications. This adds significant value beyond the bare 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?

The description clearly states the tool's purpose: 'Get the average league rankings from the API.' It specifies the verb ('Get') and resource ('average league rankings'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'getAverageStats' or 'getTeams', which might also retrieve statistical data, leaving some ambiguity about when to choose this specific tool.

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?

The description provides implied usage context by explaining the scoring system and data structure, suggesting it's for retrieving rotisserie fantasy league rankings. However, it lacks explicit guidance on when to use this tool versus alternatives like 'getAverageStats' or 'getTeams', and doesn't mention any prerequisites or exclusions, leaving the agent to infer the best scenario for its use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AsafShai/nba-fantasy-israel-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server