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Live gym / CoRec occupancy

recwell_occupancy

Check real-time headcount and capacity for every Purdue RecWell gym space. Filter by name or sort by busiest to see how crowded a facility is now.

Instructions

Real-time headcount and percent-of-capacity for every counted Purdue RecWell space — CoRec fitness floors, tracks, courts, climbing walls, pools. Answers 'how busy is the gym right now'. Source: Purdue RecWell live facility counts (updates ~every 30s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort order. Default 'busiest'.
queryNoFilter by space name, e.g. 'fitness', 'climbing', 'track', 'gym'.
open_onlyNoHide closed spaces. Default true.
Install Server

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the data source (Purdue RecWell live facility counts), update frequency (~every 30s), and the caveat 'every counted space'. This adds meaningful context about data recency and scope, though it doesn't address potential error states or data unavailability.

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 two sentences, front-loaded with the key output, and contains no redundancy. Every phrase adds value: real-time, headcount, percent-of-capacity, scope, source, and update frequency.

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 simple read-only lookup with optional filters and no output schema, the description fully conveys return semantics (headcount and percent), data freshness, and the user-facing question it answers. Given 100% parameter schema coverage, the description is self-sufficient.

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?

All three parameters (sort, query, open_only) have descriptions in the JSON schema, achieving 100% coverage. The tool description adds only minor extra meaning (e.g., example query values) beyond the schema, so the baseline of 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 clearly states the tool provides real-time headcount and percent-of-capacity for Purdue RecWell spaces, using the verb 'answers' and enumerating specific areas (fitness floors, tracks, courts, climbing walls, pools). This unambiguously distinguishes it from sibling tools focused on other domains like dining or transit.

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 clear usage context by stating it answers 'how busy is the gym right now' and scopes itself to RecWell facilities. However, it doesn't explicitly mention alternatives such as dining_line_length for real-time crowding in dining venues, so it only partially satisfies the 'when vs alternatives' guidance.

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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