search_blog

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

No annotations, so the description carries the full burden. It does disclose the source/content domain (Naver blog, genuine Korean-language user posts), which helps an agent judge result nature, but says nothing about pagination limits, result counts, language of returned text, or failure modes.

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?

Front-loaded with the action and resource, and short. The emoji and the Korean parenthetical partly duplicate the English text, which is minor waste rather than bloat.

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?

A simple read-only search with fully documented parameters; the description covers what content and from where. The main residual gap is that no output schema exists and the return shape isn't hinted at, though for a search tool that is reasonably inferable.

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 description coverage is 100% – sort, query, start, and display all have their own descriptions (defaults and enum meanings included). The description adds no parameter detail, so the baseline 3 applies.

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?

States a specific verb and resource (search personal blogs and reviews) plus the content types it surfaces, and the Korean parenthetical identifies the source as Naver Blog. An agent can tell this apart from search_news or search_image by content type, though no sibling is named explicitly.

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 list 'product reviews, personal stories, detailed tutorials, real user opinions' implies when this tool fits, which is genuine usage context. But with many near-siblings (search_cafearticle, search_kin, search_webkr) the description never says which one to pick instead – routing is left to inference.

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