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Glama

SportScore

get_standings

Get the current standings table for a league or competition by slug (e.g. 'premier-league', 'la-liga', 'nba').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCompetition slug.
sportYesSport to query. One of football, basketball, cricket, tennis.

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It correctly implies a read-only operation (getting standings). It lacks details on data freshness, permissions, or side effects, but for a simple query tool, the transparency is adequate.

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, direct sentence with no excess information. It front-loads the key action and resource, making it efficient for an AI agent.

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?

Given the tool has only 2 simple parameters and no output schema, the description is largely complete. It could optionally hint at the structure of the returned standings (e.g., team, points), but the current level is sufficient for most agents.

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?

Schema coverage is 100%; both parameters are described. The description adds value by providing examples of slugs and the context of leagues/competitions, helping the agent choose correct values 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 action ('Get'), the resource ('current standings table'), and the scope ('for a league or competition by slug'), with concrete examples. It distinguishes clearly from siblings like 'get_bracket' or 'get_match_detail'.

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 on how to use the tool (by slug) and gives examples. However, it does not explicitly state when not to use it or mention alternative tools for specific needs (e.g., match details).

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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TDQS

A4/5.0
Disambiguation5/5

Each tool targets a distinct aspect of sports data: brackets, match details, match lists, players, standings, team schedules, top scorers, and live trackers. No functional overlap exists.

Naming Consistency5/5

All tools follow a consistent 'get_<descriptive_noun>' pattern in snake_case, making the set predictable and easy to navigate.

Tool Count5/5

With 8 tools, the surface covers a broad range of sports queries without being overwhelming. Each tool serves a clear purpose.

Completeness4/5

Covers core workflows (matches, standings, players, schedules, brackets, scorers, live tracking). Lacks a direct search for slugs of teams/players/leagues, relying on get_matches results, which is a minor gap.

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