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DanielTomaro13

sportsdata-mcp

apisports_football_standings

Read-onlyIdempotent

Get football league standings by league and season, showing ranks, points, goal difference, form, and home/away splits. Pass league ID and season year.

Instructions

League table. Both league and season are required.

Returns: {response:[{league:{id, name, standings:[[{rank, team:{id, name}, points, goalsDiff, group, form, status, description, all:{played, win, draw, lose, goals:{for, against}}, home:{…}, away:{…}}]]}}]} — SHAPE FROM VENDOR DOCS. NOTE the DOUBLE nesting: standings is a list OF TABLES (one per group in a group stage), each a list of rows.

NOTE: this shape is from the vendor's documentation and has NOT been verified against a live response (we hold no key for this provider). Treat it as approximate — inspect the actual payload before relying on a field name.

Example: Premier League table {"league": 39, "season": 2023}

Auth: needs your own key in API_SPORTS_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamNoOnly this team's row.
leagueYesLeague id.
seasonYesSeason starting year.
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds meaningful context by revealing the unverified return shape, explaining the double nesting of tables, and warning that field names should be treated as approximate. This goes beyond the 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 a brief opening, a compact return-shape block, and clear notes. It is somewhat lengthy due to the detailed shape, but every section serves a purpose, and the caveat is valuable.

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 absence of an output schema, the description thoroughly documents the return structure, including nested arrays and potential grouping. It also covers required parameters, an example, and auth requirements, making it fairly complete for a read-only standings tool.

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 the schema fully describes league, season, and team parameters. The description provides an example and repeats requiredness but doesn't add substantive new meaning beyond the schema's existing descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'League table' clearly identifies the resource and implies a read operation for football standings. It specifies required parameters and provides an example, but it doesn't use a verb like 'get' and doesn't explicitly differentiate from sibling standings tools such as pl_standings or laliga_standing.

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 states that both 'league' and 'season' are required, gives a concrete example, and mentions the need for an API key, providing clear usage context. However, it does not explicitly describe when to use this tool over alternatives or when not to use it.

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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