Skip to main content
Glama

Lumify Sports Intelligence

by ai.lumify

get_splits

Read-onlyIdempotent

Get public betting splits (bets% and handle%) for an event: a consensus split plus a per-bookmaker breakdown (bookmakers[].bookmaker uses the same odds slugs as get_odds, e.g. draftkings — not upstream short keys like dk), with a captured_at timestamp. Available for MLB, NBA, NHL, NFL, NCAAF, and NCAAB. Not available for tennis or soccer (upstream 400). Returns available:false with no charge if splits haven't been captured for this event yet or the sport is unsupported.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_idNo
availableNo
consensusNoAggregate bets%/handle% across bookmakers.
bookmakersNoPer-bookmaker splits. Each item has bookmaker (odds slug), name, moneyline, spread, total.
captured_atNo

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable detail beyond that: no charge when splits are unavailable, upstream 400 errors for unsupported sports, and the bookmaker slug mapping convention relative to get_odds.

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?

The description is dense and front-loaded with the core purpose, followed by availability and fallback behavior. The first sentence is long with parentheticals, but every part earns its place; it could be slightly restructured for readability without losing content.

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 one-parameter read-only tool with an output schema, the description is complete: it states what is returned, which sports are supported, when no data is available, and the billing behavior. No critical calling context 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 fully covers the only required parameter, event_id, with guidance to source it from list_events, query_events, or search results. The description does not add extra parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.

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 begins with a specific verb and resource: 'Get public betting splits (bets% and handle%) for an event'. It clearly distinguishes the tool from siblings like get_odds by describing the exact data returned: consensus split, per-bookmaker breakdown, and captured_at timestamp.

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 provides clear usage context by listing supported sports (MLB, NBA, NHL, NFL, NCAAF, NCAAB) and explicitly stating tennis and soccer are not available. It also explains when the tool returns available:false, but it does not name an alternative tool or explicitly say when to prefer get_splits over get_odds.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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