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Lumify Sports Intelligence

get_team

Read-onlyIdempotent

Get a single team profile with its home venue. Raises a not-found error if team_id doesn't exist. Resolve ids via list_teams.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idYesTeam id, from list_teams.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
cityNo
nameNo
slugNo
sportNo
venueNo
leagueNo
divisionNo
image_urlNoLumify media URL for the team logo (https://lumify.ai/media/teams/{sport}/{id}.png). Null until ingest. Never a vendor CDN.
is_activeNo
conferenceNo
abbreviationNo

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already signal read-only, idempotent, non-destructive behavior. The description adds value by disclosing the not-found error behavior for invalid team_id, which the annotations do not cover, and notes the return includes home venue.

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 short sentences, each carrying distinct information: purpose, error behavior, and id resolution. No filler or repetition of annotation fields.

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 one required parameter, a full output schema, and strong annotations, the description covers the essential call pattern and failure mode. Nothing needed to invoke correctly is missing.

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?

The schema already documents team_id as an id from list_teams, so the baseline is 3. The description adds meaning by warning that a nonexistent team_id triggers an error and reinforces the resolution path, going slightly beyond the schema.

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 uses the specific verb 'Get' with the resource 'single team profile' and adds 'with its home venue' to define scope. It contrasts with list_teams by indicating a single team, and the explicit mention of list_teams helps disambiguate from that sibling.

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 a clear prerequisite sequence: resolve ids via list_teams before calling get_team. It implies use when a single team profile or home venue is needed, though it does not enumerate exclusions like team props or stats.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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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.