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get_ride_status

Check if a Disney ride is currently open, down, or closed. Get live status, standby wait time, and today's park hours by park and ride name.

Instructions

Check whether a Disney ride is currently operating, down, or closed.

Use this for "is X broken / open / running right now" questions. Returns the live status straight from ThemeParks.wiki along with the ride's current standby wait and today's park hours for context.

Args: park: Park name, alias, or ThemeParks.wiki entity ID. One of: Magic Kingdom, EPCOT, Hollywood Studios, Animal Kingdom, Disneyland (CA), California Adventure. ride: Attraction name as a person would say it; near-misses and partial names such as "space mountain" or "tron" are matched.

Returns: A dict with the resolved park and ride names, the status code and a plain-English gloss of it, the standby wait in minutes (None when the ride is not posting one), and today's opening/closing times.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parkYes
rideYes
Behavior5/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It reveals that the data is live from ThemeParks.wiki, what is returned (status code, plain-English gloss, standby wait, park hours), and that wait times may be None when not posted. It also explains fuzzy ride-name matching, which is useful behavioral context beyond the schema.

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 well-structured and front-loaded: the key use case appears first, followed by the live data source, then the two arguments, then the detailed return value shape. Each sentence contributes useful information without unnecessary filler.

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 that there is no output schema and no annotations, the description gives enough context for the agent to call the tool correctly: park values, ride matching behavior, semantic meaning of status and wait, and the shape of the response. It lacks the exact status-code enumerations, but the gloss is mentioned so the agent can interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description fully compensates for the 0% schema coverage. It explains that park accepts names, aliases, or entity IDs, lists valid Disney parks, and clarifies that ride accepts natural-language names and near-misses. This is far richer than the bare 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 a precise verb and resource: 'Check whether a Disney ride is currently operating, down, or closed.' This clearly defines the tool's purpose and differentiates it from get_wait_time, since the core output is ride status rather than only a wait time.

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?

It explicitly says to use this for 'is X broken / open / running right now' questions, which is strong usage guidance. It doesn't explicitly tell the agent to use get_wait_time when only wait time is needed, but the primary use case is clear enough that an agent can infer the distinction.

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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