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alondmnt

Joplin MCP Server

by alondmnt

edit_note

Precision-edit a note's body by replacing, deleting, appending, or prepending text without reading or replacing the full content.

Instructions

Precision-edit a note's body without reading or replacing the full content.

Preferred over update_note for targeted text changes — no get_note round-trip needed. Use update_note instead when changing metadata (title, todo status, due date) or replacing the entire body.

Modes:

  • Replace: provide old_string and new_string to replace text in the note body.

  • Delete: provide old_string and set new_string to '' to remove text.

  • Append: set position='end' (old_string must be None) to append new_string.

  • Prepend: set position='beginning' (old_string must be None) to prepend new_string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
note_idYesNote ID to edit
new_stringYesReplacement text (use '' to delete)
old_stringNoText to find and replace (None for positional insert)
replace_allNoReplace all occurrences (default: False)
positionNoInsert position: 'beginning' or 'end' (only when old_string is None)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Describes operational modes and parameter behaviors (e.g., new_string='' for delete, position only when old_string=None) but omits edge cases like behavior when old_string not found or concurrency effects. Given no annotations, this is a solid disclosure.

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?

Concise, well-structured: one-line summary, usage guidance, bulleted modes. Every sentence earns its place 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?

Covers all modes, parameter constraints, and usage guidelines. Output schema exists, so return values are not needed. No gaps in essential context for an AI agent to correctly 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?

Schema coverage is 100%, baseline 3. Description adds value beyond schema by explaining mode interactions (e.g., position constraints, delete via empty string) and practical usage semantics, raising the score to 4.

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?

Description clearly states 'precision-edit a note's body without reading or replacing the full content,' identifying the tool's purpose and distinguishing it from update_note for metadata or full body replacement. Lists modes (Replace, Delete, Append, Prepend) for specificity.

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?

Explicitly states when to use this tool over update_note: 'Preferred over update_note for targeted text changes — no get_note round-trip needed. Use update_note instead when changing metadata or replacing entire body.' Also provides mode-specific usage instructions.

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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