AniList MCP Server

The AniList MCP Server bridges the gap between language models and the rich anime‑centric data hosted on AniList. By exposing a collection of tools that mirror the public API, it enables assistants such as Claude to query, retrieve, and manipulate anime, manga, character, staff, studio, and user information directly within a conversation. This removes the need for developers to write custom API wrappers or handle OAuth flows, letting them focus on higher‑level logic while the server manages authentication and rate limits.

At its core, the server offers a comprehensive search capability across multiple resource types—anime, manga, characters, staff, and studios. Each search supports advanced filtering, allowing clients to narrow results by genre, tag, status, or user‑specific criteria. Once a record is identified, tools provide detailed metadata such as episode counts, release dates, synopsis, and cast lists. For users with an AniList token, the server also exposes personal data: profile information, watch lists, and activity feeds. This duality of public and authenticated endpoints makes the MCP a versatile data source for both casual queries (“What anime is currently airing?”) and personalized recommendations (“Show me my next manga to read”).

Key features include dual transport support: the server can run as a local STDIO process or expose an HTTP endpoint, giving developers flexibility in how they integrate it into existing workflows. The HTTP mode is cloud‑ready and can be deployed on platforms like Smithery, with environment variables or custom headers handling token injection securely. Advanced tools such as , , and allow assistants to not only read but also modify user data, opening possibilities for interactive recommendation systems or automated content curation.

Real‑world use cases span from building a conversational anime assistant that can fetch up‑to‑date episode schedules, to creating a recommendation engine that pulls user watch history and suggests titles based on genre preferences. Developers can also use the server to power data‑driven dashboards, generate analytics reports from site statistics, or automate studio promotion workflows. By integrating the AniList MCP into a broader AI pipeline—paired with natural language understanding and context management—the resulting system can offer seamless, data‑rich interactions without exposing the underlying API complexities to end users.