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

With no annotations, the description carries the full burden of behavioral disclosure, and it delivers substantial insight: the tool runs in parallel, performs body-content analysis in addition to status-code checks, and explains the default Korean public-institution error indicators and how to customize them. It does not explicitly define edge-case behavior such as what counts as 'ok' on network errors or how timeout/max_workers affect results, but the core non-obvious algorithm is well exposed.

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 well-structured and economical: a one-line summary, a brief explanation of the underlying problem (fake 200s), a short bullet list for two key parameters, and a one-line return format. Every sentence earns its place and the main action is front-loaded before the explanatory paragraphs.

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?

The description is highly complete for a read-only link-checking tool: it explains the true behavior, gives configuration guidance for error indicators, and provides the return shape. It does not further define the meaning of each return field (e.g., what 'ok' and 'error' represent) nor describe timeout/max_workers semantics, but the output shape and defaults are visible in the schema, leaving only minor gaps.

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 0%, so the description must compensate. It does so for the two most nuanced parameters: use_default_indicators (toggle off to avoid cross-domain false positives) and error_indicators (add site/task-specific phrases). The urls parameter is implied by the phrase "URL 목록". However, timeout and max_workers are left entirely to the schema, and no explicit behavior is described for them, though their names and defaults are reasonably self-explanatory.

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 opens with a specific action: "URL 목록의 접속 가능 여부를 병렬로 확인한다" (checks the accessibility of a URL list in parallel). It further clarifies the purpose by explaining that it not only uses HTTP status codes but also inspects page content for known error phrases, distinguishing it from a naive link checker. This clearly differentiates it from sibling tools like check_recency and update_page_cache.

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 clear context for when this tool is appropriate: checking URL accessibility, especially when a CMS falsely returns HTTP 200 for nonexistent paths. It also gives usage guidance for the indicator knobs, such as setting use_default_indicators=False to avoid false positives in other domains, and adding error_indicators for site-specific phrases. However, it does not explicitly mention when to prefer check_links over check_recency or update_page_cache, so it stops short of a full exclusions statement.

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

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

With no annotations, the description carries full burden. It discloses the markdown table format requirements, CSS selector syntax, the behavior when selector is empty (returns a specific string without network attempt), and the JS-rendered case. It also mentions the return format, making behavior transparent.

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 a few sentences, front-loaded with the core purpose, and includes necessary operational details without excessive length. It is well-structured and easy to parse.

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?

For a tool that reads a file and checks recency, the description covers input format, edge cases (empty selector, JS-rendered), and return structure. However, it leaves parameter semantics for timeout and stale_after_months unexplained, and does not describe possible values of '판정' (judgment) field, though an output schema may cover that. Slightly incomplete but adequate.

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 0%, so description must compensate. It explains cache_file format in detail (markdown table with URL and selector columns, case-insensitive, Korean/English allowed). However, it does not explain timeout or stale_after_months beyond their names, leaving ambiguity about meaning and units. Partial compensation only.

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 clearly states the tool reads a markdown table file containing URLs and CSS selectors, extracts text from each URL via the selector, interprets it as a date, and judges recency based on stale_after_months. This is a specific verb+resource and distinguishes from siblings like check_links and update_page_cache.

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?

It explicitly explains when the tool cannot be used (empty selector or JS-rendered content) and instructs the agent to use update_page_cache after browser inspection. It doesn't explicitly compare with check_links, but the distinct purpose makes usage clear.

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

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 discloses that the tool updates a file, that keys must match column names exactly, and that unmatched URLs are skipped and reported in a 'not_found' list. However, it does not mention whether the operation is destructive (overwrites the file), whether it is idempotent, or what happens if the file or table format is invalid. These gaps are notable for a mutation tool without annotations.

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 moderately long but each sentence serves a purpose: defining the action, establishing generality, and detailing matching and error behavior. It is front-loaded with the core action and avoids filler. A slight trim could improve conciseness, but it remains efficient.

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 the tool's complexity (matching rules, multiple potential file types) and the presence of an output schema (not shown but indicated), the description covers the essential behavioral contract: how updates are applied, key matching, and not_found handling. It does not address edge cases like file existence or malformed tables, but for a general-purpose updater, it is reasonably complete.

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 0%, so the description must compensate. It effectively explains the 'updates' parameter: each item must contain 'URL' (or 'url'), and other keys correspond to table column names, with exact-match semantics. It also clarifies the 'not_found' reporting. The 'cache_file' parameter is only implied as the target file, which is adequate given its self-explanatory name.

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 clearly states the verb (갱신한다, updates) and the resource (markdown pipe table file keyed by URL). It also provides examples of use cases (cache, progress table, assignment table), making the tool's purpose unambiguous. While it doesn't explicitly contrast with siblings (check_links, check_recency), those are clearly different actions, so differentiation is implicit.

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 identifies the tool as a general-purpose utility ('범용 도구') and specifies conditions for use: updates must include 'URL', and other keys must exactly match column headers. It also explains the behavior when a URL is not found. However, it does not explicitly state when NOT to use it or recommend an alternative tool, though siblings are clearly different functions.

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