Design, art and accessibility · MCP server
AniList: MCP server, setup and security
AniList is an open-source MCP server that lets an AI agent retrieve anime and manga information through the AniList API. Its repository has 92 stars and its last commit is from July 13, 2026 and it is licensed under MIT.
| Adoption (stars) | 20/40 | |
|---|---|---|
| Maintenance | 18/30 | |
| Framework (licence, registry, official) | 26/30 |
What AniList does
According to its repository: “Anime and manga information from the AniList API.”
An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. AniList is listed under Design, art and accessibility, a category of 95 servers in the directory, where it ranks #34 by stars.
Topics declared by the repository: anilist, anime, api, manga.
Installing and connecting AniList
Distribution modes declared in the official MCP registry:
- npm package (Node.js, usually started with
npx) - Docker (OCI) image
- MCPB bundle (one-click install in Claude Desktop)
- remote server (streamable-http): you connect to it by URL, with nothing to install locally
Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: TypeScript.
The registry lists 3 secret variables to provide (API key, access token). Keep them in the client's environment variables, never in a shared file or a repository.
Security: what to check
An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.
Points found for AniList:
- Secrets: the server acts with your credentials. Create a dedicated key with minimum rights and an expiry date.
Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.
Project activity and maintenance
Created on March 20, 2025, the yuna0x0/anilist-mcp repository has 92 stars and 19 forks, with 1 open issues. It has more stars than 65% of the servers in the directory. Last change 3 months ago. Version published in the registry: 1.4.0 (November 28, 2025).
Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.
Verdict
AniList is a reasonable choice; check the points below before connecting it.
Strengths
- Listed in the official MCP registry
- Usable remotely, without installation
- Permissive licence (MIT)
Limitations
- Requires an API key or token
Avoid if…
- You do not want to give credentials to an agent
Alternatives
- Pascal 3D Editor25.1K ★
- Next AI Draw.io36.4K ★
- Measure1.4K ★
- TerraVision1.7K ★
- Davinci resolve MCP3.7K ★
- MeiGen AI Design MCP1.8K ★
Learn more
Frequently asked questions
How do I install AniList?
AniList is distributed as npm package (Node.js, usually started with npx), Docker (OCI) image, MCPB bundle (one-click install in Claude Desktop), and a remote server. Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of yuna0x0/anilist-mcp.
Is AniList free?
The code is published under the MIT licence and can be used free of charge. It needs an API key: the service it connects to may be paid.
Is AniList safe?
Points to watch: secrets. Give it minimum rights and approve sensitive actions.
Is it an official server?
It is published in the MCP registry under the verified namespace ai.smithery, which shows the publisher controls that domain.
What are the alternatives to AniList?
In the same category: Pascal 3D Editor, Next AI Draw.io, Measure, and TerraVision. Pascal 3D Editor and Next AI Draw.io score higher on our grid.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score