Lumify Sports Intelligence

get_player

Read-onlyIdempotent

Get a single player identity profile: name, sport, country, position/handedness, physical stats, current team, rankings (tennis {singles, points}; null on every other sport), and image_url (Lumify media URL, null until the sport's headshot/enrichment job). Raises a not-found error if player_id doesn't exist. Resolve ids via search_players.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`player_id`	Yes	Player id, from search_players.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No	Lumify player ID.
`slug`	No	URL-safe unique slug.
`sport`	No	Sport slug, e.g. mlb, tennis, nfl.
`position`	No	Roster position abbreviation when the sport has one (e.g. C, P, SS for MLB; QB, WR for NFL). Null for tennis and when not yet ingested.
`rankings`	No	Sport ranking block. Tennis: {singles, points} from ATP/WTA standings. Null for every other sport. Tennis /stats also exposes ranking / ranking_points on the tennis player block.
`birthdate`	No	YYYY-MM-DD date of birth.
`full_name`	No	Display name.
`height_cm`	No	Height in centimetres. Null when not ingested.
`image_url`	No	Lumify media URL for the player headshot (https://lumify.ai/media/players/{sport}/{id}.{ext}). Null until the sport's headshot/enrichment job. Never a vendor CDN.
`is_active`	No	False when deactivated or retired.
`last_name`	No
`weight_kg`	No	Weight in kilograms. Null when not ingested.
`first_name`	No
`handedness`	No	left \| right \| switch. Meaning is sport-specific (bats for MLB, shoots for NHL, playing hand for tennis). Null when unknown.
`retired_at`	No	YYYY-MM-DD retirement date when known.
`country_code`	No	ISO 3166-1 alpha-3 country code.
`current_team_id`	No	Lumify team ID when the player is on a club roster. Null for tennis.
`current_team_name`	No	Current team display name. Null for tennis.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses important runtime behavior: it raises a not-found error for invalid player_id, and it explains null semantics for rankings on non-tennis sports and image_url before enrichment. This gives the agent accurate expectations for edge cases and return values.

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 information-dense but well-structured and front-loaded. Every sentence earns its place: the first defines the tool and its output fields, the second states error behavior, and the third gives id resolution guidance. There is no redundant or filler 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?

Given the single required parameter, a high-coverage schema, an output schema, and strong annotations, the description is complete. It covers the return payload's special cases, the error case, and how to obtain a valid player_id. An agent has everything needed to call this tool correctly.

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 input schema already documents player_id with 100% coverage, including 'from search_players.' The description reinforces this by saying 'Resolve ids via search_players,' but it does not add meaningful new parameter semantics beyond what the schema provides. The error behavior is useful context but is more behavioral than parameter-specific.

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 states a specific verb and resource: 'Get a single player identity profile', then enumerates the fields returned (name, sport, country, position/handedness, physical stats, current team, rankings, image_url). It clearly distinguishes from siblings like get_team, get_player_events, and get_player_props by scoping the tool to identity/profile data rather than events, odds, or props.

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 context for when to use this tool: to retrieve a single player's identity profile, and it explicitly routes id resolution via search_players. It does not explicitly state when not to use it or name alternatives for events/props, but the profile-scoped wording and the mention of search_players provide adequate guidance.

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 tools Inspect

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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.

get_player

Input Schema

Output Schema

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