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Lumify Sports Intelligence

by ai.lumify

list_teams

Read-onlyIdempotent

List teams, paginated by id (after_id). Filter by sport, league, conference, division, country, active status, or name (q, partial match). Returns each team's id, slug, name, city, conference/division, venue, and image_url (Lumify media URL, null until ingest). Use get_team for full detail on one id once resolved here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoTeam name search (partial match).
limitNoMax teams to return per page.
sportNoSport slug, e.g. nfl, nba, soccer.
activeNoFilter by active status.
leagueNoLeague slug, e.g. nfl, mls.
countryNoISO country code, e.g. USA.
after_idNoCursor: last team id from the previous page's next_after_id.
divisionNoDivision name, e.g. AFC East.
conferenceNoConference name, e.g. AFC, Eastern.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
has_moreNo
next_after_idNoPass as after_id to fetch the next page; null on the last page.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description only needs to add extra context. It adds useful behavioral details: pagination is driven by after_id, results are a summary set, and image_url is null until ingest.

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?

Three sentences with no filler; the main action and pagination are front-loaded, followed by filters, return fields, and the sibling-tool handoff. Every sentence earns its place.

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 list endpoint with an output schema and annotations covering safety and idempotency, the description covers how to paginate, what filters exist, what fields come back, and how to get full detail afterwards. Nothing essential 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?

Schema description coverage is 100%, so the schema already documents all nine optional parameters. The description mostly restates the filter categories and cursor semantics rather than adding new parameter-level meaning, which is adequate but not additive.

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?

States a specific verb and resource (List teams), names the pagination mechanism, enumerates filters, and specifies the returned fields. It also distinguishes itself from get_team, so an agent can tell the two apart.

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?

Provides clear context for when to use this tool: resolve a team id here, then call get_team for full detail. This explicit routing to a sibling tool eliminates ambiguity about the boundary between list and detail operations.

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