note.com MCP Server

unlike-note

Remove a like from a note.com article using its article ID. This tool helps manage article interactions by deleting previously added likes.

Instructions

記事のスキを削除する

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "noteId": "<noteId>"
    },
    "name": "unlike-note"
  }
}

Input Schema

Name	Required	Description	Default
`noteId`	Yes	記事ID

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.0.0Jan 3, 2026
- addedInput schema / $schema
```
Added value: +"http://json-schema.org/draft-07/schema#"
```
- addedInput schema / additionalProperties
```
Added value: +false
```
First observedNov 11, 2025

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the action (delete a like) but doesn't disclose behavioral traits such as permissions required, whether the operation is idempotent, error conditions (e.g., if the note doesn't exist or isn't liked), or what happens on success (e.g., no return value or confirmation). For a mutation tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence in Japanese that directly states the tool's purpose. It's front-loaded with no unnecessary words, making it highly concise and well-structured for quick understanding.

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?

Given the complexity (a mutation tool with no annotations and no output schema), the description is incomplete. It lacks details on behavioral aspects like error handling, return values, or side effects. While the purpose is clear, the tool's full context isn't adequately covered for safe and effective use by an AI agent.

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?

The description doesn't add any parameter semantics beyond what the input schema provides. The schema has 100% description coverage with 'noteId' clearly documented as '記事ID' (note ID). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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?

The description '記事のスキを削除する' (Delete a note's like) clearly states the action (delete) and resource (note's like). It distinguishes from siblings like 'like-note' (which adds likes) and 'get-likes' (which retrieves likes). However, it doesn't specify the exact resource type (e.g., 'unlike' vs 'remove like'), making it slightly less specific than a perfect 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., a note must exist and be liked first), exclusions, or comparisons to sibling tools like 'like-note' or 'get-likes'. The agent must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.