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Lumify Sports Intelligence

by ai.lumify

list_ev

Read-onlyIdempotent

Beta. List pregame main-line +EV opportunities for a predictive-framework sport (soccer, mlb, tennis, nfl, ncaaf), sorted by ev_pct descending. market=h2h (default, moneyline), spreads, or totals. Tennis totals are not offered (Stage 1 is moneyline + spreads). Same gates as bets[].ev on get_intelligence: sharp-fair price gap, positive and ≤25%, suppressed MLB moneyline null books skipped in favor of the next eligible book. 1 credit. Field catalog: https://lumify.ai/docs/reference#intelligence-ev

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bookNoRestrict opportunities to one sportsbook slug (e.g. fanduel).
limitNoMax opportunities to return (1–200). Default 50.
sportYesSport slug: soccer, mlb, tennis, nfl, or ncaaf.
leagueNoOptional league slug (mls, epl, atp, …). Soccer without a league scans every published soccer league (MLS + big-five).
marketNoMain-line family: h2h (default, moneyline), spreads, or totals. Tennis + totals returns 400.
min_evNoMinimum EV% to include. Default 0. Clamped to 0–25.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
betaNoAlways true.
bookNoBook filter, or null.
sportNoSport slug this scan was run for.
totalNoCount of opportunities on this page.
leagueNoLeague filter, or null for the whole sport.
marketNoh2h, spreads, or totals — the family scanned.
max_evNoStale-line cap (25).
min_evNoMinimum EV% applied.
opportunitiesNoPositive-EV main-line rows: {event_id, sport, league, commence_time, home_team, away_team, side, team, bet_type, line, fair_probability, ev}.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds substantial context beyond that: the tool costs 1 credit, is in Beta, sorts by ev_pct, suppresses MLB moneyline null books, skips to the next eligible book, applies sharp-fair price gates, and returns 400 for tennis totals.

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 well-organized, front-loading the core purpose and then adding constraints, gates, cost, and a reference link. Every sentence earns its place; there is no 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?

With an output schema present, the description does not need to explain return values. It covers supported sports, market families, default behavior, edge cases, EV gates, cost, and links to the full field catalog, making it fully actionable for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description goes beyond the schema by explaining the market default, the tennis totals 400 error, the MLB null-book behavior, and the soccer league scanning behavior, adding useful semantics for parameter selection.

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 clearly states the tool lists pregame main-line +EV opportunities, names the supported sports, and specifies sorting by ev_pct descending. It also differentiates the tool from siblings by mentioning market families and the tennis totals exclusion.

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 clear context for when to use the tool: it is for pregame +EV opportunities, not live events, and it specifies market defaults and unsupported combinations. It references get_intelligence for the shared EV gates, which helps an agent relate the tool to a sibling, though it does not explicitly say 'use this instead of X'.

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