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alondmnt

Joplin MCP Server

by alondmnt

restore_from_trash

Restore a deleted note or notebook from Joplin's trash. Restoring a notebook does not restore its sub-items; restore each individually.

Instructions

Restore a note or notebook from Joplin's trash.

Restores a previously deleted item by setting its deleted_time back to 0. The item reappears in its original notebook.

Scope of restore (important):

  • Only the single item identified by item_id is restored. When restoring a notebook, its sub-notebooks and the notes inside stay trashed and must each be restored individually. Joplin sets deleted_time on every descendant when a notebook is trashed, and this tool clears it on one item per call.

  • If the original parent notebook is also trashed, restore the parent first or the restored item may not be visible.

To find descendants to restore after restoring a notebook, use find_notes(query="*", trash=True) and filter to the relevant subtree.

Returns: str: Success message confirming the item was restored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
item_idYesNote or notebook ID to restore
item_typeNoItem type: 'note' or 'notebook'. Restoring a notebook does not restore the items inside it.note

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 clearly explains the behavior: sets deleted_time to 0, does not restore sub-items of a notebook, and requires parent restoration first. This is thorough for a mutation tool.

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 well-structured: a clear first sentence, followed by essential details in bullet-like paragraphs, and ends with the return type. Every sentence adds value without redundancy.

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 the tool's complexity and the presence of an output schema (str), the description covers purpose, usage guidelines, behavioral details, and parameter context. It is complete for an agent to use the tool correctly.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description does not add new parameter semantics beyond the schema; it mostly repeats the item_type description. The behavioral context about restoration scope is useful but does not enhance parameter understanding.

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 explicitly states 'Restore a note or notebook from Joplin's trash' with a specific verb and resource. It is clearly distinct from sibling tools which involve creation, editing, searching, etc.

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 provides comprehensive guidance: when to use (restore trashed items), key limitations (only single item restored, parent must not be trashed), and an alternative (use find_notes to find descendants). It explicitly states what not to expect, aiding correct tool selection.

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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