get_entityMetadata for any theme park entity (destination, park, attraction, restaurant, or show): name, type, location, timezone, and parent ids.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | Yes | An entity id from list_destinations or get_wait_times. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "entity_id": "space-mountain"
+ },
+ {
+ "entity_id": "disney-world"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. Description adds value by specifying the exact fields returned and the types of entities covered, providing context beyond the 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?
Single sentence, no redundant information, front-loaded with key details. Highly concise.
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?
For a simple read-only tool with one parameter and no output schema, the description is adequately complete. It could mention error handling or output format, but the core functionality is clear.
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 description coverage is 100% with a clear description of entity_id. The tool description does not add new semantic information for the parameter beyond what the schema already provides.
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?
Description clearly states it returns metadata for theme park entities and lists specific fields (name, type, location, timezone, parent ids) and entity types. It distinguishes from siblings like get_wait_times but could be more explicit about how it differs from entity_profile or resolve_entity.
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?
No explicit guidance on when to use this tool versus alternatives. The schema description hints at where to get entity IDs but does not provide usage context or when-not-to-use.
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.