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SupaPark — Walt Disney World live data

Hardest dining reservations

get_hardest_dining_reservations
Read-only

Walt Disney World restaurants ranked by how rarely a reservation opens, from SupaPark's own availability checks: difficulty tier, the share of checks that found a table, and cancellations ("drops") caught in the last 30 days.

Input Schema

NameRequiredDescriptionDefault
parkNoFilter to restaurants in this park.
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real context beyond that: the data provenance ('SupaPark's own availability checks'), the freshness window ('last 30 days' for drops), and the meaning of each reported field, which is valuable since there is no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that leads with the resource and ranking metric before the supporting detail. Dense but every clause (tier, share of checks, drops) carries information; the parenthetical gloss on 'drops' is minor padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only list tool with no output schema, the description covers what is returned and the ordering. It leaves the default result size (limit=10) and whether ranking is global or per-park unstated, but nothing critical to invoking it correctly is missing.

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?

Schema coverage is 50%: 'park' is documented in the schema with an enum, while 'limit' carries only constraints (default 10, max 25) and no description. The description mentions neither parameter, so it adds nothing over the structured fields; baseline 3 is appropriate given the schema still conveys the enum and bounds.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (Walt Disney World restaurants) and the ranking criterion (how rarely a reservation opens), plus the fields produced. It implicitly separates itself from the per-restaurant odds tool by being a ranked list, but never names get_dining_drop_odds or watch_dining as the alternative for narrower needs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use or when-not-to-use guidance is given. With siblings like get_dining_drop_odds and watch_dining in the same domain, the agent gets no signal about which one to pick for a specific restaurant versus a global ranking.

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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