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lumifyai

Lumify Sports Intelligence

query_events

Read-onlyIdempotent

Query sports events with natural language phrases such as 'live nfl games today' or 'college basketball this week'. No need for structured filters; the tool recognizes sport, status, and date mentions.

Instructions

Search events with a natural-language query instead of structured filters — e.g. 'live nfl games today' or 'college basketball this week'. Rule-based (not an LLM): recognizes sport (nfl/nba/mlb/nhl/tennis/soccer/ncaaf/ncaab + aliases like hockey, american football, college basketball), status (live/final/upcoming/…), dates (today/tomorrow, this week, next N days, YYYY-MM-DD ranges). Bare 'football' is ambiguous and left unrecognized. Response includes interpreted filters, equivalent REST call, and unrecognized_terms. Prefer list_events when you already know the structured filters you want.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoOverrides any limit parsed from the query text. Max 100.
queryYesFree text, e.g. 'live nfl games today'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoThe original natural-language query text.
totalNo
eventsNo
interpretedNoThe list_events-equivalent filters parsed from the query text (sport, status, date, from, to, limit).
next_after_idNo
equivalent_requestNoThe literal GET /v1/events request this query was translated to.
unrecognized_termsNoQuery words that didn't map to a known filter.
Behavior5/5

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

Reveals rule-based (not LLM), lists recognized sports/status/dates, mentions ambiguity of 'football', and describes output fields beyond what annotations provide.

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?

Concise 3-4 sentences, front-loaded with purpose, no unnecessary words.

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 annotations, input/output schemas, and sibling context, description is complete: covers usage, limitations, and behavior.

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?

Schema coverage is 100%; description adds value by explaining query as natural-language with examples and limit as override. Good but not exhaustive on format.

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 clearly states the tool does natural-language event search, with examples like 'live nfl games today' and distinguishes from list_events.

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?

Explicitly says 'Prefer list_events when you already know the structured filters you want', guiding when to use this tool vs. the sibling.

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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