Skip to main content
Glama

Lumify Sports Intelligence

by ai.lumify

batch_get_events

Read-onlyIdempotent

Get multiple events by id in one call — for agents that already have a list of ids and want full detail for each without one call per event. Max 25 ids. Returns full detail for every id that exists plus a not_found list for any that don't (never billed). Use get_event for a single id, or list_events / query_events to discover ids first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bookmakerNoBookmaker for inlined odds and intelligence market prices. Defaults to pinnacle. Valid: pinnacle, fanduel, draftkings, betmgm, caesars, bet365, circa, hardrock, betonline, all.
event_idsYesEvent ids to fetch (max 25); duplicates are billed once.
include_oddsNoInline current odds scoped by bookmaker (default: pinnacle). Does not add credits — each found event stays 1 credit.
include_intelligenceNoInline bet intelligence on each event. Does not add credits.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalNo
eventsNoFull event detail (same shape as get_event) for every found id.
not_foundNoRequested event_ids that don't exist. Never billed.

TDQS

A4.7/5.0
Behavior5/5

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

The description adds meaningful behavior beyond the readOnlyHint, openWorldHint, and idempotentHint annotations: found events return full detail, missing events return a not_found list, and missing events are never billed. It also sets the 25-id cap. The description does not contradict any annotation.

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 compact, with only three sentences: one for purpose and usage, one for limits and return behavior, and one for sibling guidance. The main scoping constraint is front-loaded and no sentence is wasted.

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 full parameter documentation in the input schema, the description provides exactly what the structured fields cannot: selection criteria, the 25-id boundary, and not-found billing behavior. An agent can decide whether to use this tool versus its siblings without needing further documentation.

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 description coverage is 100%, so all four parameters, including bookmaker and both include flags, are fully documented there. The description adds useful context about event_ids and billing behavior, but it does not need to explain each parameter again, so a baseline 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 opens with a specific action and resource, stating it gets multiple events by id in one call. It also clarifies how this differs from get_event (single event) and list_events/query_events (discovery), so an agent can identify the tool without reading sibling schemas.

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?

Explicit usage context is provided: this is for agents that already have a list of ids and want full detail without one call per event. The last sentence directly names alternatives, telling the agent to use get_event for a single id and list_events/query_events to discover ids first.

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