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rishijatia

Fantasy Premier League MCP Server

analyze_players

Filter Fantasy Premier League players by position, team, price, points, ownership, and form. Get summary statistics and gameweek data to make informed decisions.

Instructions

Filter and analyze FPL players based on multiple criteria

Args:
    position: Player position (e.g., "midfielders", "defenders")
    team: Team name filter
    min_price: Minimum player price in millions
    max_price: Maximum player price in millions
    min_points: Minimum total points
    min_ownership: Minimum ownership percentage
    max_ownership: Maximum ownership percentage
    form_threshold: Minimum form rating
    include_gameweeks: Whether to include gameweek-by-gameweek data
    num_gameweeks: Number of recent gameweeks to include
    sort_by: Metric to sort results by (default: total_points)
    sort_order: Sort direction ("asc" or "desc")
    limit: Maximum number of players to return
    
Returns:
    Filtered player data with summary statistics

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
positionNo
teamNo
min_priceNo
max_priceNo
min_pointsNo
min_ownershipNo
max_ownershipNo
form_thresholdNo
include_gameweeksNo
num_gameweeksNo
sort_byNototal_points
sort_orderNodesc
limitNo
Behavior3/5

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

No annotations provided, so description carries burden. It mentions returns 'Filtered player data with summary statistics' but lacks details on side effects, data freshness, rate limits, or pagination. Adequate but minimal.

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?

Front-loaded purpose sentence, then structured Args and Returns sections. Slightly verbose due to long parameter list, but overall concise and well-organized.

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?

With 13 parameters and no output schema or annotations, the description covers argument meanings but omits interaction logic (e.g., AND/OR filtering), default behavior examples, and comprehensive return details. Leaves gaps for a complex tool.

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

Parameters4/5

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

Despite 0% schema description coverage, the description provides clear explanations for each parameter in the Args block (e.g., 'position: Player position (e.g., midfielders, defenders)'). This adds significant value beyond 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 clearly states 'Filter and analyze FPL players based on multiple criteria', using specific verbs and resource. It distinguishes from sibling tools like compare_players and search_fpl_players by emphasizing multi-criteria filtering.

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 explicit guidance on when to use this tool versus alternatives. Does not mention when to avoid or provide comparisons to sibling tools like search_fpl_players.

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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