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alondmnt

Joplin MCP Server

by alondmnt

tag_note

Add one or more tags to one or more notes in a single operation. Returns a detailed report of successes and failures.

Instructions

Add one or more tags to one or more notes.

Both args accept a single string or a list. When either is a list, the cartesian product is applied (every tag on every note) in one call — preferred over looping.

Output: aggregated TAG_NOTE report with TOTAL_OPS / SUCCEEDED / FAILED, one row per (note, tag) pair (so the scalar case is a one-row report).

Tags must exist beforehand — use create_tag to add new ones. Missing tags are reported up front and nothing is applied. Per-op failures (e.g. invalid note ID or allowlist denial) are captured in the report; other ops still run.

Examples: - tag_note("abc...", "Work") - Tag one note with one tag - tag_note(["abc...", "def..."], "Work") - Tag two notes with one tag - tag_note("abc...", ["Work", "Urgent"]) - Add two tags to one note - tag_note(["abc...", "def..."], ["Work", "Urgent"]) - 2x2 = 4 ops

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
note_idYesNote ID, or list of note IDs
tag_nameYesTag name, or list of tag names

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses batch behavior (cartesian product), output format (aggregated report), error handling (missing tags reported upfront, per-op failures). Lacks info on idempotency or rate limits, but adds sufficient transparency 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.

Conciseness4/5

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

Description is well-structured with bullet points and examples. Front-loaded with main purpose. Each sentence is informative, but could be slightly more concise.

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 output schema exists (mentioned in context signals), description does not need to explain return values in detail but still covers output format. With 2 params fully described and rich usage examples, it is complete.

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% with descriptions in schema. Description adds value by explaining batch semantics, input format (single or list), and provides examples. Enhances understanding beyond 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?

Description starts with 'Add one or more tags to one or more notes.' Clearly states verb and resources. Differentiates from siblings like untag_note (removal) and create_tag (creating a tag).

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 says tags must exist beforehand, recommends using create_tag for new tags. Indicates batch operation is preferred over looping. Provides clear when-to-use and contextual alternative.

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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