get_wait_timesLive theme park wait times — how long is the wait for an attraction right now. Returns standby and single-rider ride queue minutes plus operating status for every attraction in a park (e.g. Disney / Universal ride queues). Pass a PARK entity_id from list_destinations.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | Yes | A PARK entity id from list_destinations. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "entity_id": "magic-kingdom"
+ },
+ {
+ "entity_id": "universal-studios-florida"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a read-only, idempotent, open-world operation with no destructive behavior. The description adds behavioral details: what data is returned (standby and single-rider queue minutes, operating status) and that it covers every attraction in a park, which adds value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: two sentences that first explain the purpose and then provide usage instructions. No extraneous information, front-loaded with key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description compensates by explaining what the tool returns (standby and single-rider queue minutes, operating status). It is adequate for a single-parameter tool, though a more detailed example of the output could improve completeness. Context signals indicate low complexity, so the description is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with the schema providing a description for entity_id ('A PARK entity id from list_destinations.'). The description reinforces this same information but does not add additional semantic detail. Baseline 3 is appropriate since the schema already covers the parameter meaning adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that it returns live theme park wait times, including standby and single-rider queue minutes and operating status for every attraction in a park. It specifies the resource (live wait times) and the action (get/return), and differentiates from siblings by focusing on wait times specifically.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool to get current wait times for a park, and requires passing a PARK entity_id from list_destinations. However, it does not explicitly state when not to use this tool or mention alternatives, which would improve guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct, well-documented purposes, but several overlap or are explicitly redundant: ask_pipeworx_beta currently behaves identically to ask_pipeworx, discover_tools and suggest_questions both serve as discovery entry points, and scan_competitor_ai_presence wraps ai_visibility_check. The thematic split between theme-park, data-lookup, prediction-market, and memory tools also forces agents to navigate unrelated clusters.
Naming is a mix of verb_noun (list_destinations, get_wait_times, remember, resolve_entity), noun_phrase (entity_profile, recent_changes, bet_research), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Within families the patterns are consistent, but across the set the conventions are inconsistent and sometimes reverse the verb/noun order, making the surface harder to predict.
35 tools is heavy, and the server is named Themeparks yet only 4 tools actually relate to theme parks. The remaining 31 tools span Pipeworx data retrieval, prediction markets, memory, subscriptions, and feedback, creating a bloated and misaligned scope. A tightly scoped theme-park server would need far fewer tools, and a general data research server would not be named Themeparks.
For the implied theme-park domain, the surface is thin: list destinations, get entity metadata, get schedule, and get wait times cover basic lookups but omit search, attraction details beyond waits, historical data, pricing, dining/show info, and park updates. The non-theme-park tools are extensive, but they do not complete the server's apparent stated purpose.