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Sportsdataio Player Props

sportsdataio_player_props
Read-onlyIdempotent

Player prop bets for a date — available betting markets and outcomes (over/under totals, payouts) across NFL / NBA / MLB / NHL / CFB / CBB. Requires a SEPARATE SportsDataIO Odds/Props API key (the Props warehouse is billed apart from Stats/Projections). Note: a free trial key returns scrambled sample data. Example: sportsdataio_player_props({ league: "nba", date: "2026-01-15", _apiKey: "your-odds-key" })

Input Schema

NameRequiredDescriptionDefault
dateYesSlate date in YYYY-MM-DD format, e.g. "2026-01-15"
leagueYesLeague to query. One of: nfl / nba / mlb / nhl / cfb / cbb.
_apiKeyYesSportsDataIO Odds/Props API key (separate from the Stats key)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld/destructive=false, so the safety profile is covered. The description adds meaningful non-schema context: a SEPARATE Odds/Props API key is required because props are billed apart, and a free-trial key returns scrambled sample data — an important caveat that prevents misreading results.

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?

Three sentences, front-loaded with the resource and scope, followed by the key caveat and a concrete call example. Slightly dense but every clause carries information an agent needs; nothing is redundant padding.

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 still sketches the return content (available markets, over/under totals, payouts) and the required auth. For a single-fetch, 3-param read tool this is nearly complete, though it omits pagination or date-range behavior.

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% and each parameter (date format, league enum, _apiKey purpose) is already documented in the schema. The description reinforces the key distinction but adds no format or semantics beyond what the schema provides, so baseline 3 applies.

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?

States a specific verb+resource: 'Player prop bets for a date — available betting markets and outcomes (over/under totals, payouts)'. It also names the covered leagues, which cleanly separates it from sibling tools like sportsdataio_projections and sportsdataio_injuries without needing to reference them by 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 context is implied through the described scope (odds/props data for a given slate date), and the separate-key requirement is stated. However, it never explicitly names an alternative tool or a when-not-to-use condition, so the agent must infer routing from the sibling names alone.

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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