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Lumify Sports Intelligence

by ai.lumify

get_period_odds

Read-onlyIdempotent

Get first-half / first-five / first-set lines and live progress for an NFL, NCAAF, NBA, NCAAB, MLB, soccer, or tennis event. Joins persisted first_half_spreads / first_half_totals mains to this-event period scores (1H = Q1+Q2, NCAAB or soccer native 1H; MLB F5 = innings 1–5; tennis S1 = first-set games) and grades the period, not the full game. GET /odds / get_odds stay on moneyline/spread/total. Returns available:false with no charge if no period 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, mlb, soccer, or tennis.
periodNoCurrent period from the live score snapshot.
statusNoEvent status (scheduled, inprogress, delayed, final, …).
event_idNoLumify event ID.
availableNoFalse when no first-half / first-five / first-set mains have been ingested; period_odds is empty and the call isn't billed.
period_oddsNoOne row per (scope, market, line). Fields: scope (1H, F5, or S1), market (first_half_spreads / first_half_totals), line (home-perspective for spreads), home_score, away_score, current (totals only), current_margin (spreads only), pct_of_line, settleable, scope_complete, result (totals), home_result / away_result (spreads), books ({book_slug: {home, away} or {over, under}}).

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool as read-only/idempotent/non-destructive, and the description adds meaningful behavior beyond that: it joins persisted period mains to live scores, grades the period rather than the full game, explains the no-charge available:false state, and documents the HTTP 400 restriction. No contradiction with 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?

Every sentence earns its place and the main purpose is front-loaded. The description is somewhat dense and the phrase 'GET /odds / get_odds' is slightly awkward, but overall it is efficiently structured for the amount of domain nuance it conveys.

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?

Given the complexity of period definitions and sport-specific rules, the description is remarkably complete. It covers supported sports, how period scores are computed, the difference from full-game grades, the no-charge empty state, and the error case. With an output schema present, 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 has 100% coverage for the single required parameter, event_id, and already explains where it comes from. The description does not add parameter-specific detail, but the schema carries the full burden, so the baseline of 3 is appropriate.

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?

The description states a specific verb ('Get'), a specific resource ('first-half / first-five / first-set lines and live progress'), and the sports covered. It explicitly distinguishes itself from get_odds, which stays on moneyline/spread/total, so an agent can immediately tell what this tool is for.

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?

The description says when to use this tool: when period-specific lines/progress are needed, not full-game odds. It names the alternative (GET /odds / get_odds) and gives exclusion signals such as 'Other sports return HTTP 400' and 'available:false with no charge if no period mains have been ingested.'

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