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Grinv

AniList MCP Server

Get a user's most recent activity

get_user_recent_activity
Read-only

Retrieve the 5 most recent activity posts from any AniList user (ID or username), including list updates, text posts, and received messages. No need to search first.

Instructions

Get an AniList user's 5 most recent activity posts, with full content (not just IDs) — every activity type (list updates, text posts, and message activity they received; the underlying query applies no type filter of its own), a fixed count, not configurable; use get_user_activity for a paginated full feed instead. Accepts an exact AniList username directly — no need to call search_user first unless you only have a partial/fuzzy name (username resolution costs one extra internal lookup either way).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
userYesAniList user ID, or username. Unlike search_activity's own `user` filter, both an unknown numeric ID and an unknown username error here rather than silently returning an empty feed.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
activityYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and openWorldHint annotations, the description adds behavioral context: full content not just IDs, no type filter (even message activity received), fixed count, and that unknown IDs/usernames error rather than returning empty results (from param description). It also mentions an extra internal lookup for username resolution. This adds substantial transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with every clause earning its place: purpose, type coverage, count fixity, alternative, and parameter guidance. No fluff, and the main action is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with an output schema, the description covers all needed context: exact behavior (count, type coverage, full content), error semantics, alternative tool, and parameter resolution. Nothing important is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already covers the 'user' parameter 100%, including the accepted types and error behavior. The description adds practical semantics: 'Accepts an exact AniList username directly — no need to call search_user first' and clarifies the cost of username resolution, which complements the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Get an AniList user's 5 most recent activity posts' and clarifies scope with details like full content, all activity types, and fixed count. It also distinguishes from sibling get_user_activity by explicitly naming that alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool vs alternatives: 'use get_user_activity for a paginated full feed instead' and when to consider search_user for partial/fuzzy names. This gives clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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