ThemeParks.wiki API MCP Server

The ThemeParks.wiki MCP server transforms the rich, real‑time data of the ThemeParks.wiki API into a set of ready‑to‑use tools for AI assistants. It exposes key pieces of information—operating hours, attraction wait times, and show schedules—through a simple, declarative interface that Claude or any MCP‑compliant client can call directly. By turning external park data into first‑class tools, the server removes the need for developers to write custom HTTP clients or parse raw JSON responses; instead they can focus on crafting higher‑level conversational flows.

At its core, the server offers a concise toolkit of six actions:

• getEntityChildren – lists attractions and shows within a specified park.
• getEntityScheduleForDate – retrieves daily operating hours for any park on a given date.
• getAllParks – returns the full catalogue of parks, including names and identifiers.
• getParksByName – filters the park list by name or resort, simplifying discovery.
• getEntity – fetches detailed metadata for a single entity (park, attraction, or show).
• getEntityLive – delivers live wait times for attractions or show schedules for shows.

These tools are intentionally lightweight, each accepting only an entity ID (and a date where relevant) and returning structured data that can be consumed directly by an AI assistant’s dialogue logic. This design aligns with the MCP principle of exposing domain knowledge as callable resources, enabling seamless integration into conversational agents without extra parsing overhead.

The practical value shines in travel‑planning and guest‑experience applications. For example, a chatbot can ask a user for their destination park, then invoke getEntityScheduleForDate to inform them of opening hours on a future date. It can also call getEntityLive to provide up‑to‑minute wait times for a particular ride, helping guests decide whether to queue or explore other attractions. Because the server abstracts the API layer, developers can add new features—such as itinerary suggestions or real‑time alerts—without touching the underlying network code.

Integration with AI workflows is straightforward. Once the server is registered in a client configuration, any MCP‑enabled assistant can call these tools as part of its reasoning cycle. The assistant’s prompt can include a structured request, and the server will return JSON that the model can interpret or pass to downstream logic. This tight coupling allows for dynamic, data‑driven conversations that feel natural and authoritative.

What sets this MCP server apart is its focused domain expertise combined with minimal friction. It requires only a Java runtime (or Docker) to run, yet delivers the full breadth of ThemeParks.wiki’s data through a clean, declarative API. Developers building AI assistants for travel, hospitality, or entertainment sectors can therefore rapidly prototype and deploy park‑centric features—such as itinerary planners, wait‑time alerts, or themed recommendations—without managing external API credentials or handling data transformations.