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unfile_papers

Remove papers from a specific Zotero collection while preserving the item, its other collections, and sent state. Use for misfiled items; refs matched exactly.

Instructions

Remove papers from one Zotero collection, and nothing else.

The inverse of file_papers, for misfiled items: membership in the named collection is dropped, while the item, its other collections (including the Reading Queue), and its sent-state are untouched. The Reading Queue itself is refused as a target — leaving the queue is remove_from_queue's job, with its keep-or-trash safeguards. To move a paper between collections, file it into the new one and unfile it from the old. Refs match like remove_from_queue: exact arXiv id, DOI, URL, or title, against the collection's items; an ambiguous ref (matching several items) removes nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refsYes
collectionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries full burden. It transparently discloses that only membership in the named collection is dropped, while other collections and sent-state remain untouched. It also explains the behavior for ambiguous refs (removes nothing).

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 fairly concise for the amount of detail, but it could be slightly shorter. The key action is front-loaded, and additional context is provided in subsequent sentences. The structure is logical but includes some extra detail that might be condensed.

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?

Given no annotations and partial schema, the description covers purpose, usage guidelines, behavioral details, parameter semantics, and edge cases (e.g., ambiguous refs, refusal of Reading Queue). It is complete enough for an AI agent to correctly select and invoke the tool.

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 input schema defines refs (array of strings) and collection (string) with 0% coverage. The description adds meaning by explaining that refs match exact arXiv id, DOI, URL, or title, and that collection is the target collection. This compensates for the schema's lack of description, though more detail on collection identification would be helpful.

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 clearly states the tool removes papers from one Zotero collection, using the verb 'Remove' and specifying the resource 'papers from one Zotero collection'. It distinguishes itself from siblings like file_papers and remove_from_queue by highlighting its inverse nature and specific scope.

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 explains when to use the tool (for misfiled items) and when not (Reading Queue is refused as a target, with a pointer to remove_from_queue for that purpose). It also provides instructions for moving papers between collections and clarifies ambiguous ref handling.

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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