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universal-mcp

Fpl Universal MCP Server

fpl_analyze_player_fixtures

Check a player's upcoming fixtures and get a difficulty rating to inform fantasy football decisions.

Instructions

Analyze upcoming fixtures for a player and provide a difficulty rating

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
player_nameYesPlayer name to search for
num_fixturesNoNumber of upcoming fixtures to analyze (default: 5)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It states the core action and output but does not mention read-only behavior, how the difficulty rating is computed, what scale is used, or any side effects. This leaves important behavioral ambiguity for an agent invoking the tool.

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 a single, front-loaded sentence with no filler. Every word contributes to stating the tool's purpose and output, making it easy for an agent to parse quickly.

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?

For a simple two-parameter tool, the description is mostly sufficient to invoke the tool correctly. However, since there is no output schema, the return shape is under-specified: 'a difficulty rating' could mean per-fixture ratings, an aggregate rating, or a numeric scale. The core contract is clear, but the result interpretation has gaps.

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

Parameters3/5

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

Schema coverage is 100%, so both parameters are already documented in the input schema. The description adds no additional meaning about the player_name matching behavior or the num_fixtures range. Per the rubric, the baseline of 3 is appropriate when the schema already carries the parameter semantics.

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 uses a specific verb ('Analyze'), a clear resource ('upcoming fixtures for a player'), and an expected output ('difficulty rating'). It distinguishes itself from siblings like fpl_analyze_fixtures and fpl_analyze_players by explicitly scoping the analysis to a single player's fixtures.

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 implies when to use the tool: when a player-specific fixture difficulty analysis is needed. However, it provides no explicit when-not guidance or comparison to sibling tools such as fpl_analyze_fixtures, fpl_analyze_players, or fpl_get_player_information.

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