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DanielTomaro13

sportsdata-mcp

mysportsfeeds_standings

Read-onlyIdempotent

Get league standings for a season. Specify league and season to retrieve team rankings, win/loss records, and division/conference ranks.

Instructions

Standings for a league and season.

Returns: {teams:[{team:{id, abbreviation, city, name}, stats:{standings:{wins, losses, winPct, gamesBack}}, divisionRank:{rank, gamesBack}, conferenceRank:{…}, overallRank:{…}}]} — SHAPE FROM VENDOR DOCS.

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: This season's standings {"league": "nba", "season": "current"}

Auth: needs your own key in MYSPORTSFEEDS_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamNoTeam abbreviations.
leagueYesLeague. One of: nfl, nba, mlb, nhl. Required — part of the URL path.
seasonYesSeason string. Required — part of the URL path.
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description goes beyond by disclosing that the return shape is unverified against a live response, warning the agent to treat it as approximate, and stating the auth key requirement. This adds valuable context about reliability and prerequisites.

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-organized: summary, return shape, caveat, example, and auth. Each section adds value, and the format is easy to parse. Slightly longer than necessary but all content is relevant.

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?

For a tool with no output schema, the description provides an approximate return shape, which helps the agent understand expected data. It also includes an example and auth requirements. Missing details like full season format options (e.g., '2024-2025') are minor; overall it's sufficiently 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.

Parameters4/5

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

Schema coverage is 100%, but the description adds an example showing a concrete invocation (league: 'nba', season: 'current') and the fact that 'current' is a valid season value. This clarifies usage beyond the schema's generic 'Season string' description.

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 clearly states the tool returns standings for a league and season, with a concrete return shape and example. It is specific about the resource (standings) and scope, though it doesn't explicitly distinguish from sibling standings tools beyond the provider prefix in the name.

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?

Usage is implied through the example and first line, and the auth note explains a prerequisite. However, there are no explicit exclusions or comparisons to alternative standings tools (e.g., pl_standings, nhl_standings), which would help an agent choose this tool over others.

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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