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seasonal_candidates

Fetch and filter seasonal anime candidates by year, season, and source, then apply residual preference heuristics to surface the most relevant options for adding to your collection.

Instructions

Fetch server-filtered seasonal anime candidates and apply residual preference heuristics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo
limitNo
fieldsNo
seasonNo
sourceNomyanimelist
max_pagesNo
source_sortNoanime_num_list_users
include_skippedNo
min_num_list_usersNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations, the description must carry the full safety and behavioral burden. 'Fetch' suggests a read operation, but 'apply residual preference heuristics' is opaque and could imply state changes, yet no side effects, modifications, or side-effect-free claims are disclosed.

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

Conciseness4/5

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

The description is a single concise sentence that leads with the primary verb and resource. It is not bloated, though the word 'residual' is vague jargon that slightly undermines the clarity of the concise structure.

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

Completeness2/5

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

For a complex tool with 9 unannotated parameters and no guidance, a one-sentence description is incomplete. The meaning of server-filtering, the heuristics, and how they map to the listed parameters is unknown; an agent cannot confidently invoke the tool correctly.

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

Parameters2/5

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

Schema description coverage is 0% while there are 9 parameters, so the description must compensate but does not. Terms like 'seasonal' and 'preference heuristics' loosely imply year/season and source_sort/min_num_list_users, but no parameter is explicitly mapped or explained.

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

Purpose4/5

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

Uses the specific verb 'Fetch' with a clear resource and scope, indicating that this tool returns a 'candidates' list for a season. However, the phrase 'residual preference heuristics' is vague, and it doesn't explicitly distinguish itself from the sibling 'seasonal_anime'.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives like seasonal_anime or prepare_seasonal_review. The description implies a filtered candidate pipeline, but does not state which scenario should call this tool.

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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