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nguyenanhducs

Fantasy Premier League MCP Server

fpl_find_fixture_opportunities

Read-onlyIdempotent

Analyzes upcoming fixture difficulty, identifies teams with easiest schedules, and recommends their top players by position.

Instructions

Find teams with the easiest upcoming fixtures and their best assets.

Analyzes fixture difficulty for all 20 teams over the next N gameweeks. Identifies teams with the most favorable schedule and recommends their top-performing players (filtered by position if requested).

Args: params (FindFixtureOpportunitiesInput): Validated input parameters containing: - num_gameweeks (int): Number of gameweeks to analyze (3-10) - max_teams (int): Number of teams to recommend (1-5) - positions (list[str] | None): Optional position filter

Returns: str: Analysis of best teams to target and their key players

Examples: - Target next 5 GWs: num_gameweeks=5 - Find best attackers: positions=['Midfielder', 'Forward']

Error Handling: - Returns error if data unavailable - Returns formatted error message if API fails

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's behavioral context is complementary. It adds details about error handling (returns error if data unavailable) and output format (analysis string), which are useful beyond annotations.

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 well-structured with sections for purpose, arguments, returns, examples, and error handling. It is informative without being verbose, though the Args section could be slightly more compact.

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 (analysis of 20 teams over multiple gameweeks), the description covers all necessary aspects: purpose, parameters, examples, error handling, and output format. Annotations confirm it is read-only and idempotent, and the output schema exists, so no further return details are needed.

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?

The input schema provides thorough descriptions for all parameters (e.g., num_gameweeks, max_teams, positions). The tool description does not repeat these but adds practical examples (e.g., 'Target next 5 GWs: num_gameweeks=5'), which add usage context 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 the verb (find/analyze/identify/recommends) and resource (teams with easiest upcoming fixtures and best assets). It distinguishes from siblings like fpl_analyze_team_fixtures (specific team) and fpl_get_fixtures_for_gameweek (raw fixtures) by focusing on analysis and recommendations across all teams.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool (analyze fixture difficulty for all teams and get recommendations) but does not explicitly mention when not to use it or compare to alternatives. The examples illustrate typical use cases, enhancing clarity.

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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