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Lumify Sports Intelligence

by ai.lumify

get_player_props

Read-onlyIdempotent

Get player-prop lines and live progress for an NFL, NCAAF, NBA, NCAAB, NHL, or MLB event. Joins persisted player-prop mains to this-event player box counts and grades over/under/push (1:1 slugs, combo sums, weighted total bases, anytime TD, double-double/triple-double, hockey points). Sport × market catalog: https://lumify.ai/docs/player-props. GET /odds / get_odds stay on moneyline/spread/total. Returns available:false with no charge if no prop mains have been ingested. Other sports return HTTP 400.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_idYesEvent id, from list_events, query_events, or search results.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
clockNoGame clock from the live score snapshot.
sportNoSport slug: nfl, ncaaf, nba, ncaab, nhl, or mlb.
periodNoCurrent period from the live score snapshot.
statusNoEvent status (scheduled, inprogress, delayed, final, …).
event_idNoLumify event ID.
availableNoFalse when no player-prop mains have been ingested; player_props is empty and the call isn't billed.
player_propsNoOne row per (player, market, line). Fields: player, player_id (null if unmatched), market (prop category), line, current (this-event box count), pct_of_line, settleable, result (in_progress / over / under / push, or null), books ({book_slug: {over, under}} American prices).

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already signal read-only and idempotent behavior, but the description adds substantial non-obvious context: joining persisted prop mains to box counts, grading various prop types, returning available:false without charge when no prop mains exist, and rejecting unsupported sports. Nothing contradicts 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every clause earns its place: sports scope, output contents, prop types covered, sibling distinction, no-charge behavior, and error handling are all stated with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An agent has enough information to call the tool correctly: which sports/events are valid, what categories of props are graded, when not to expect results, and which sibling tools to use instead. The presence of an output schema covers return structure, so nothing essential 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?

The schema already fully documents event_id, including its source from list_events, query_events, or search results. The description adds useful sport-level context but does not materially extend the meaning of the single parameter 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?

States a specific verb and resource: retrieving player-prop lines and live progress, and enumerates the supported sports. It also differentiates itself from get_odds by explicitly saying that odds tools stay on moneyline/spread/total.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Names the alternative tool category (GET /odds / get_odds) and clarifies that those tools cover moneyline/spread/total, making the selection boundary clear. It also explains the no-charge available:false case and the HTTP 400 behavior for unsupported sports.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Lumify Sports Intelligence toolsInspect
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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct data resource or operation: events, live scores, odds, odds history, splits, stats, intelligence, player props, players, teams, sports, and seasons. Pairs like list_events vs query_events and get_event vs get_live_score are clearly differentiated by structured vs natural-language filtering and lightweight vs full detail.

Naming Consistency5/5

Tool names consistently follow a verb_noun snake_case pattern: get_*, list_*, search_*, query_*, batch_get_*, and estimate_cost. The naming conventions make the resource family immediately obvious, and deviations like batch_get_events are still predictable variants.

Tool Count4/5

19 tools is on the higher side, but each tool covers a specific sports-intelligence data product or workflow with little redundancy. The count feels intentional for the breadth of the domain rather than bloated.

Completeness4/5

The surface covers event discovery and retrieval, live scores, odds and line movement, splits, statistics, player props, intelligence, player/team/sport/season lookups, batch fetching, and cost estimation. Minor gaps like team standings or full rosters are not exposed, but core agent workflows are well supported.

Resources