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Sportsdataio Injuries

sportsdataio_injuries
Read-onlyIdempotent

Current injuries in the NFL / NBA / MLB / NHL / CFB / CBB — returns each injured player with team, position, status (Out, Questionable, etc.) and the body part affected. Note: a free SportsDataIO trial key returns scrambled sample data, not real injuries. Example: sportsdataio_injuries({ league: "nfl", _apiKey: "your-key" })

Input Schema

NameRequiredDescriptionDefault
leagueYesLeague to query. One of: nfl / nba / mlb / nhl / cfb / cbb.
_apiKeyYesSportsDataIO API key (free trial at sportsdata.io/developers)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish the safe, idempotent, open-world read profile, so the bar is lower. The description adds real value beyond them: the shape of the response (per-player team/position/status/body part) and the behavioral warning that a free trial key returns scrambled sample data rather than real injuries.

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?

Front-loaded with the resource and coverage, then the return fields, then caveats. The trailing JSON example is redundant with the schema's own examples, so one sentence is slightly wasteful, but overall it is tight and readable.

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?

With no output schema, the description carries the return-value burden and does describe the per-player fields returned. Combined with a fully-covered input schema and clear annotations, an agent has enough to call it correctly; only explicit sibling disambiguation is missing.

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 description coverage is 100% — the league enum and the _apiKey description are fully documented in the schema, and the description's inline example merely duplicates the schema examples. Baseline 3 is correct since the schema does all parameter-level work.

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?

States a specific resource (current injuries) with the exact set of leagues covered and enumerates the returned fields (team, position, status, body part). This is far more specific than a tautology, but it never names the closest siblings (sportsdataio_player_props, sportsdataio_projections) to disambiguate which sportsdataio tool to reach for.

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 — you call it to get injuries for a league — but there is no explicit when/when-not guidance or comparison to the sibling sportsdataio tools. The trial-key note is a useful caveat but is about data quality, not about when to select this tool.

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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