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dohyung1

FPL Intelligence

squad_scout

Scout your FPL squad with hidden data fields to uncover blank gameweek warnings, set piece takers, suspension risks, expected points, ICT breakdown, and points per million rankings.

Instructions

Deep scout report using FPL's hidden data fields most managers don't know about.

USE THIS WHEN the user asks: "Any hidden insights?", "Set piece takers?",
"Suspension risks?", "What does FPL's own data say?", or for a deeper dive
beyond what fpl_manager_hub provides.

Surfaces: blank GW warnings, FPL's expected points (ep_next), set piece duties,
yellow card suspension risks, ICT breakdown, points per million rankings.

Args:
    team_id: FPL team ID (the number in your FPL URL).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idYes
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 explains the kind of data returned (blank GW warnings, FPL's expected points, ICT breakdown, etc.) but does not mention any side effects, safety, rate limits, or potential errors.

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

Conciseness5/5

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

The description is concise and well-structured. It uses a clear opening line, usage guidelines, a bullet list of surfaced insights, and a parameter explanation. Every sentence adds value.

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 tool's complexity and the absence of an output schema, the description comprehensively covers purpose, when to use, what data it surfaces, and the parameter description. It provides enough context for an AI agent to select and invoke correctly.

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 only parameter, team_id, has no description in the schema. The description adds meaningful context: 'FPL team ID (the number in your FPL URL)'. This fully compensates for the 0% schema coverage.

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 it's a 'Deep scout report' using hidden FPL data and lists specific insights (blank GW warnings, expected points, set piece duties, etc.). It differentiates from sibling tools by mentioning it goes beyond what fpl_manager_hub provides.

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 explicitly says 'USE THIS WHEN' and provides example user queries ('Any hidden insights?', 'Set piece takers?', etc.). It also hints at when not to use by contrasting with fpl_manager_hub.

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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