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Lumify Sports Intelligence

by ai.lumify

get_team_props

Read-onlyIdempotent

Get team-total lines and live progress for an NFL, NCAAF, MLB, or soccer event. Joins persisted Pinnacle team-total mains (each team's full-game points/runs/goals Over/Under) to this-event participant scores and grades over/under/push. GET /odds / get_odds stay on moneyline/spread/total. Returns available:false with no charge if no team-total 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, mlb, or soccer.
periodNoCurrent period from the live score snapshot.
statusNoEvent status (scheduled, inprogress, delayed, final, …).
event_idNoLumify event ID.
availableNoFalse when no team-total mains have been ingested; team_props is empty and the call isn't billed.
team_propsNoOne row per (team, line). Fields: team, team_id, side (home/away), market (team_total), line, current (this-event team score), 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 establish read-only, idempotent, and non-destructive behavior. The description adds meaningful operational context: joining persisted Pinnacle team-total mains to live scores, grading over/under/push, and no-charge behavior when data is unavailable. This goes well beyond the annotations without contradicting them.

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 four tightly packed sentences with no filler. The main action is front-loaded, and every clause adds new information: supported sports, data join logic, sibling differentiation, and edge-case behavior.

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?

With an output schema present and only one well-documented parameter, the description covers all essential selection and invocation context. It includes sport restrictions, no-data behavior, and the relationship to get_odds, so nothing critical 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 only parameter, event_id, is already fully described in the schema with sourcing guidance ('from list_events, query_events, or search results'). The description's supported-sport scope is useful but does not add parameter-level mechanics, so the high schema coverage 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 names a specific resource ('team-total lines and live progress') and scopes it to NFL, NCAAF, MLB, and soccer events. It explicitly contrasts with get_odds's moneyline/spread/total scope, so an agent can distinguish it from siblings.

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?

It states when to use this tool for team-total props and indicates that GET /odds and get_odds cover moneyline/spread/total instead. It also gives practical fallback/edge behavior: available:false with no charge when no team-total mains exist, and HTTP 400 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