Low Risk

anilist_recommendations

Get community recommendations for a specific anime or manga. Use when the user asks for shows similar to a specific title, or says "I liked X, what else should I watch?" Returns titles ranked by recommendation count with format, score, and genres.

Part of the Ani server.

anilist_recommendations is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

SECURE ANI →

Free to start. No card required.

AI agents call anilist_recommendations to retrieve information from Ani without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though anilist_recommendations only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "anilist_recommendations": {}
  }
}

See the full Ani policy for all 55 tools.

Get this rule live on your own Ani server in minutes. PolicyLayer enforces it on every call, before it runs.

ENFORCE ON MY ANI →

View all 55 tools →

These attack patterns abuse exactly the kind of access anilist_recommendations gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so anilist_recommendations only ever does what you allow.

SECURE ANI →

Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the anilist_recommendations tool do? +

Get community recommendations for a specific anime or manga. Use when the user asks for shows similar to a specific title, or says "I liked X, what else should I watch?" Returns titles ranked by recommendation count with format, score, and genres.. It is categorised as a Read tool in the Ani MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on anilist_recommendations? +

Register the Ani MCP server in PolicyLayer and add a rule for anilist_recommendations: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Ani. Nothing to install.

What risk level is anilist_recommendations? +

anilist_recommendations is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit anilist_recommendations? +

Yes. Add a rate_limit block to the anilist_recommendations rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block anilist_recommendations completely? +

Set action: deny in the PolicyLayer policy for anilist_recommendations. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides anilist_recommendations? +

anilist_recommendations is provided by the Ani MCP server (gavxm/ani-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Ani tool call.

Deterministic rules across all 55 Ani tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.