Disney Parks MCP Server
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- AlicenseNot gradedqualityCmaintenanceEnables access to live theme park wait times, schedules, and attraction data from ThemeParks.wiki.1 npmMIT
- FlicenseAqualityBmaintenanceProvides on-demand Disney ride status and standby wait times for supported parks, resolving park and ride names by alias or partial match.2-
- FlicenseNot gradedqualityBmaintenanceEnables querying 80+ theme parks and destinations worldwide for their attractions, live ride wait times and statuses, and operating hours or show schedules through the ThemeParks parksapi library.-
- AlicenseNot gradedqualityCmaintenanceProvides keyless access to a fan-built API of animated films, characters, locations, species, and vehicles, enabling list retrieval, single-item lookups, and plain-language queries through MCP tools.2 npmMIT
- AlicenseNot gradedqualityCmaintenanceSearch and query California State Parks open geospatial datasets (parks, trails, recreational layers) via ArcGIS Feature Services using natural language.3 npmMIT
- AlicenseAqualityCmaintenanceDisney Lorcana TCG MCP server -- card search, deck analysis, and franchise browsing powered by LorcanaJSON.744 npm3MIT
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no significant overlap. For example, 'discover' handles semantic/conceptual queries, 'search' handles exact name lookups, and tools like 'find_attractions' and 'find_dining' target specific resource types. The descriptions explicitly differentiate between similar tools (e.g., 'discover' vs. 'search'), preventing agent confusion.
Most tools follow a consistent verb_noun pattern (e.g., 'find_attractions', 'find_dining', 'list_parks', 'search'), but 'discover' and 'initialize' deviate slightly by using single verbs without nouns. However, these deviations are minor and still readable, maintaining overall clarity without chaotic mixing of conventions.
With 7 tools, the count is well-scoped for the Disney parks domain, covering core operations like data initialization, park listing, attraction/dining discovery, and health checks. Each tool earns its place by addressing specific needs, such as semantic search, exact lookups, and filtered queries, without being overly sparse or bloated.
The tool surface covers essential CRUD-like operations for the domain, including initialization, listing, searching, and filtering for attractions and dining. Minor gaps exist, such as no explicit update or delete tools for dynamic data (e.g., ride wait times), but agents can work around this using the provided tools for discovery and status checks.