like_article
Toggle like status on an article by providing its ID. Use to like or unlike content on Alog platform.
Instructions
記事にいいね / いいね解除(トグル)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| article_id | Yes | 記事ID |
Toggle like status on an article by providing its ID. Use to like or unlike content on Alog platform.
記事にいいね / いいね解除(トグル)
| Name | Required | Description | Default |
|---|---|---|---|
| article_id | Yes | 記事ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the toggle behavior (like/unlike), which is a key behavioral trait. However, no annotations exist, and the description does not address authentication requirements, side effects, or error conditions, leaving gaps in 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (one short phrase) with no wasted words. It is front-loaded with the essential action and toggle nature, making it efficient for agent parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, output schema, or extra context, the description lacks completeness. It does not explain the return value, confirmation of action, error responses, or prerequisites (e.g., login required), leaving the agent underinformed for a state-changing operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'article_id' described as '記事ID'. The description adds no additional meaning beyond what the schema already provides, so it meets the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description '記事にいいね / いいね解除(トグル)' clearly states the verb (like/unlike) and resource (article), and explicitly notes it is a toggle action. This distinguishes it from siblings like 'bookmark_article' or 'follow' which are different interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 such as 'bookmark_article' or 'follow'. There is no mention of prerequisites, context, or exclusion criteria, leaving the agent to infer usage.
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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