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soccer-mcp

get_fixtures

Retrieve all football fixtures for any date across every competition, including final scores when played, with optional league filter and finished-match-only mode.

Instructions

All football fixtures of one day (YYYY-MM-DD) across every competition, with scores when played.

Args: date: ISO date, e.g. 2026-09-20 league: optional case-insensitive substring filter on the competition name only_finished: only return matches with a final score

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
leagueNo
only_finishedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses the broad scope, the conditional inclusion of scores, and the effect of only_finished. It does not mention edge cases like timezone handling or live-match behavior, but these are secondary for a read-only fixtures query.

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 main behavior is front-loaded in one clear sentence, followed by a compact parameter list where each line adds meaningful detail. There is no filler or redundant repetition of schema types.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

All three parameters are explained, an output schema exists, and the tool's scope is unambiguous. The only notable gap is the absence of a brief pointer to a sibling tool like get_results when result-specific data is needed, but that is not required to call this tool 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?

Schema description coverage is 0%, and the description fully compensates: date gets an ISO format with an explicit example, league is defined as a case-insensitive substring filter on the competition name, and only_finished is explained as returning only matches with a final score.

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 names a specific operation: retrieving all football fixtures for one date across every competition, with scores when played. It states the resource and scope clearly, and the date-focused framing helps distinguish it from result- or bet-oriented siblings.

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?

It clearly establishes when to use the tool: when a caller wants all fixtures for a single day, optionally filtered by league or finished matches. It does not explicitly contrast it with get_results or state when not to use it, so it stops short of full guidance.

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