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Lumify Sports Intelligence

by ai.lumify

get_stats

Read-onlyIdempotent

Get raw, deterministic team/match statistics for a soccer, MLB, tennis, NFL, NCAAF, NBA, NCAAB, or NHL event — no odds (use get_odds) and no scoring (use get_intelligence). Payload is sport-specific: teams.home/away except tennis (players.player_1/player_2). Tennis doubles/qualifying return available:false. Form/record/H2H exclude walkovers and NFL/NCAAF/NBA/NCAAB preseason. Other sports return HTTP 400. Field catalog: https://lumify.ai/docs/reference#event-stats

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
matchNoTennis only. Match context: surface, competition_id/name, tier, round (ingested labels like 'Round of 16'/'Quarterfinal'/'Final', not 'R16'), court (not currently populated), status, result_type, scoreboard (sets_won + per-set games; tiebreak_score not currently populated).
teamsNoSoccer/MLB/NFL/NCAAF/NBA/NCAAB/NHL only (home/away). Each side includes image_url (Lumify media URL, null until ingest; never a vendor CDN). Soccer: recent_form, team_strength, venue, rest_days, sos, lineup, rates_l5, rates_season. MLB: record, recent_form, rest_days, rates_l5/rates_season (incl. OBP/SLG/OPS/RBI/TB), starting_pitcher (post-box only), lineup, player_box (this event's per-player batting/pitching counting stats). NFL/NCAAF: record (points_for/against), recent_form (points_scored/allowed), rest_days, rates_l5/rates_season (total/passing/rushing yards, attempts, turnovers, third_down_pct), player_box (one players[] line per athlete with passing/rushing/receiving/defense/kicking counts; final only). NBA/NCAAB: record (points_for/against), recent_form (points_scored/allowed), rest_days, rates_l5/rates_season (rebounds/assists/steals/blocks/turnovers/fouls, fg_pct/three_point_pct/free_throw_pct as ratio-of-sums), player_box (points/rebounds/assists/…; final only). NHL: record (goals_for/against), recent_form (goals_scored/allowed), rest_days, rates_l5/rates_season (shots_on_goal/hits/penalty_minutes/power_play_goals/giveaways/takeaways/blocked_shots), player_box (skaters[] + goalies[]; assists/hits remapped from assists_nhl/hits_nhl; final only). Settleables stay on get_player_props. Absent on tennis responses — use players instead.
playersNoTennis only (player_1/player_2). Each side: player_id, name, country_code, ranking, ranking_points, rest_days, recent_form, surface_form, record (tour_lookback wins/losses), career_rates (Stage-2 serve/return percentages from RapidAPI Tennis API; null until ingest), career_surface (Stage-2 match-surface W/L over windows.career_surface_years; null when empty). History windows exclude walkovers (unplayed) and include retirements.
profileNoSoccer only. 'world_cup' or 'club'.
windowsNoExplicit sample depths. Soccer: recent_form (5), rates_l5 (5), rates_season ('season'), head_to_head (10), sos (5). MLB/NFL/NCAAF/NBA/NCAAB/NHL: recent_form (5), rates_l5 (5), rates_season ('season'), head_to_head (10). Tennis: recent_form (10), surface_form (10), head_to_head (10), record_lookback_days (365), career_surface_years (3).
event_idNoLumify event ID.
availableNoFalse when participants haven't resolved, or tennis draw_type is not exactly 'singles' (doubles/qualifying out of scope); every other field is omitted and the call isn't billed.
draw_typeNoTennis only. Always 'singles' when available is true. Values other than 'singles' (including 'doubles' and 'qualifying') return available:false.
league_slugNoLeague/tour slug, e.g. 'mls', 'mlb', 'nfl', 'atp', 'wta'.
head_to_headNoPast meetings: {window, meetings, total}. Soccer: {home_goals, away_goals}. MLB: {home_runs, away_runs}. NFL/NCAAF/NBA/NCAAB: {home_points, away_points}. NHL: {home_goals, away_goals}. Tennis: {event_id, starts_at, surface, winner_role, sets_won} relative to this fixture's player_1/player_2 (walkovers excluded).
neutral_siteNoSoccer only. Whether this fixture is at a neutral venue. Informational here; factored into home-advantage scoring on get_intelligence.
league_contextNoSoccer only. {avg_goals_per_team}: league-wide baseline goals/team/game, used by get_intelligence's attack/defense scoring. Null for fixed-baseline competitions.

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description adds significant behavioral detail: deterministic results, sport-specific payload shape, tennis exception handling, exclusion of walkovers and preseason stats, unsupported-sport error behavior, and a field catalog link. This is far more transparency than annotations alone provide.

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: scope, alternatives, sport-specific payload, edge cases, error behavior, and documentation link. It is front-loaded with the core purpose and avoids filler.

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?

For a single-parameter stats tool with an output schema and readOnly/idempotent annotations, the description is complete. It covers supported sports, response shape exceptions, returns for tricky cases, error behavior, and a link to the full field catalog.

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 event_id parameter, including its source ('from list_events, query_events, or search results'). The description adds context about the response payload but not new meaning about the parameter itself, so the schema carries the burden as expected.

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 and resource: 'Get raw, deterministic team/match statistics' for a defined list of sports. It explicitly differentiates from siblings by saying 'no odds (use get_odds) and no scoring (use get_intelligence)', so an agent can select it confidently without opening schemas.

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

Usage Guidelines4/5

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

The description gives explicit routing to alternatives: use get_odds for odds and get_intelligence for scoring. It also warns that unsupported sports return HTTP 400 and that tennis doubles/qualifying return available:false. It does not mention the live-score sibling get_live_score, but the core when/when-not guidance is clear.

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