jejusns

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the auth requirement but says nothing about return values, side effects, idempotency, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a notable gap.

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?

Two short sentences, front-loaded with the core action and followed by the permission note. Every word earns its place, though the brevity borders on under-specification given the missing behavioral and sibling-related context.

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?

No output schema, no annotations, and a close sibling (post_as_persona) that is not differentiated. The description does not explain the response format, how it differs from persona-based post creation, or any behavioral effects. Incomplete for an agent to confidently select and invoke this tool.

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%, with title, content, and category_id all described in the input schema. The description adds no parameter-level detail beyond what the schema provides, 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?

The description '새 게시글을 작성합니다' (creates a new post) states a specific verb and resource, clearly distinguishing it from read/search siblings like get_post and search_posts. However, it does not differentiate it from the closely related sibling post_as_persona, which also creates posts but presumably with persona context.

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 includes a useful prerequisite ('write:posts 권한이 필요합니다' / requires write:posts permission) but provides no guidance on when to use this tool versus post_as_persona, no exclusions, and no alternative routing. An agent gets no decision support beyond the permission requirement.

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 provided, the description carries the behavioral disclosure burden. It clearly implies a read-only search operation, identifies the data source, notes that open_now is server-side judged in Korean time, and specifies that results include weekday hours, phone, and coordinates. This goes beyond the minimum for a non-destructive lookup 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?

Three compact sentences front-load the core purpose, then explain key filters, then summarize result contents. There is no filler, and every sentence contributes useful information.

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?

There is no output schema, but the description compensates by stating that each result includes weekday opening hours, phone, and coordinates. It also gives the data source and filter semantics. Minor gaps remain around default behavior and combined filter logic, but the overall context is strong for a search tool.

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?

The input schema already documents all parameters with 100% coverage, so the baseline is 3. The description adds a little extra meaning by explaining server-side time judgment for open_now and the Sunday-filter example, but it does not substantially deepen parameter understanding beyond what the schema provides.

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 verb and resource: '제주 병의원...검색합니다' (search Jeju hospitals/clinics and pharmacies). It includes the exact counts, the data source, and the key filtering capabilities, so an agent immediately knows what the tool does and how it differs from unrelated siblings.

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 gives clear operational guidance by explaining how to use open_now and day filters, and states the data source. There are no closely related sibling tools for facility search, so no explicit exclusion of alternatives is needed; this level of contextual guidance is sufficient.

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 present, the description carries the burden of behavioral disclosure. It communicates that the operation is a read ('조회') and that admin rights are required, which is useful. It does not describe output format, report scope, potential failure behavior, or whether the statistics are aggregated or filtered in any way.

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: two short sentences with no filler or redundant information. It front-loads the core purpose and immediately states the key access requirement, which is appropriate for a tool with no parameters.

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 zero-parameter read-only tool, the description provides a usable baseline: the agent knows what to call and under what permission condition. However, there is no output schema and the description does not explain what the returned ad performance statistics will include, which leaves some ambiguity about expected results.

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?

The input schema has zero parameters and 100% schema description coverage, so there are no parameter semantics for the description to clarify. The baseline for a no-parameter tool is 4, and the description correctly does not invent parameter details.

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 identifies the action ('조회합니다' / retrieves) and the resource ('광고 성과 통계' / ad performance statistics), so an agent can understand what the tool does. It does not explicitly differentiate it from sibling stats tools like get_site_stats or get_price_stats, but the ad-specific resource is reasonably distinctive.

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 mentions an admin permission prerequisite, which is a form of usage constraint. However, it provides no guidance on when to use this tool over the related statistics tools or which conditions should trigger this tool versus get_site_stats/get_price_stats.

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 behavioral disclosure burden, but it only says 'fetch latest feed' and 'category filter possible.' It does not disclose read-only nature, ordering behavior, default pagination, category matching semantics, or what the response contains.

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?

Two short Korean sentences are tightly packed: resource, action, and key capability. No filler or repetition.

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 read tool with no annotations and no output schema, the description is thin. An agent still does not know what a feed entry looks like, how categories are matched, or how pagination behaves.

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%, so the schema already documents page, limit, and category. The description adds only the category filtering concept, which is useful but not substantial enough to exceed the baseline 3.

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?

Description states a specific action ('가져옵니다' / fetch) on a specific resource ('제주돌담 최신 피드' / latest Jeju DolDam feed), with category filtering. This clearly distinguishes it from siblings like get_post (single post) and search_posts.

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 implies it is for browsing the latest feed with an optional category filter, but it does not explicitly state when to choose this over search_posts/search_property_listings or any exclusions. No alternative tools are named.

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 behavioral disclosure burden. The verb '조회' and tool name 'get_post' imply a read-only operation, and '전체 내용' indicates the response scope. But it does not mention authentication needs, post visibility rules, or potential side effects. The gap is moderate for a simple read 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 one short declarative sentence that front-loads the action and resource. There is no redundancy, filler, or repetition of schema details.

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 one-parameter read operation with no output schema, the description is nearly complete: it identifies the lookup key and states that the full content is returned. It lacks explicit response structure and error semantics, but those are not essential for basic invocation.

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?

The single required parameter 'id' is already fully documented in the input schema with type, format, pattern, and a Korean description, giving 100% schema description coverage. The description adds no additional semantic meaning beyond the schema, so the baseline score of 3 is appropriate.

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 uses a specific verb and resource: '게시글 ID로 전체 내용을 조회합니다' (retrieve full content by post ID). It clearly scopes the operation to a single post identified by ID, which distinguishes it from search-oriented siblings like search_posts or get_feed.

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 usage context is implied: use this tool when you already have a post UUID and need the full post content. However, it does not explicitly state when not to use it or point to alternatives such as search_posts, get_feed, or get_user_profile.

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 present, and the description carries the burden well: it discloses data provenance, clarifies it is not asking-price data, and reveals important edge-case behavior (suppressed=true with reason and fallback_region), plus the expected user-facing handling ('표본 부족' vs '데이터가 없다').

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?

Three short sentences in logical order: core promise, no-region branch, and edge-case branch. Every sentence adds information and there is no filler.

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?

With no output schema and no annotations, the description covers the main invocation branches and fallback handling well. It is slightly incomplete because it does not specify the shape of the successful stats response (e.g., metric fields, period), but the low complexity and clear branch guidance keep it strong.

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%, so the region parameter is already fully documented; the description restates the omit-to-list behavior without adding new parameter syntax or formatting. Baseline 3 applies because the schema does the heavy lifting.

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 states a concrete verb-resource pair: providing Jeju apartment real transaction price statistics based on MOLIT reported data, explicitly distinguishing '시세' from '호가'. This clearly differentiates it from sibling stats tools like get_ad_stats and get_site_stats.

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 gives explicit calling conditions: omit region to get the list of supported regions, and re-query via fallback_region when suppressed=true. It does not name sibling alternatives or explicitly say when not to use this tool, so it stops short of a 5.

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?

There are no annotations, so the description carries the burden. It discloses the admin authorization requirement and implies a read-only stats retrieval through 'statistics'. It does not describe return formatting or side effects, but for a zero-parameter stats endpoint the permission note is the key behavioral trait.

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?

A single compact sentence states the resource, gives representative contents, and notes the access requirement. Every element earns its place with no redundancy.

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 no-argument, no-output-schema tool, the description is largely complete: it identifies the data domain, representative return values, and admin restriction. It does not document the exact response schema, but the examples are enough for an agent to invoke and interpret results.

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?

The tool has zero parameters, so the baseline for parameter semantics is 4. The description adds no parameter details, but none are needed because the schema already shows an empty properties object and the description covers the resource.

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 states the resource ('site-wide statistics') and gives concrete contents (user, post, and comment counts), distinguishing it from siblings like get_ad_stats and get_price_stats by the word 'site-wide'. The verb is implicit in the tool name, but the noun and examples make the purpose unambiguous.

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 gives clear context: use this when overall site-wide counters are needed, and it warns that admin permission is required. It does not explicitly contrast with sibling tools or state when not to use it, so it stops short of a 5.

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 for behavioral disclosure. It does indicate that this is a read operation ('조회') and that results are limited to public profile data with personal information excluded, which is useful. However, it does not describe response format, authentication, or error behavior, leaving some behavioral ambiguity.

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 single concise sentence that front-loads the core action and then adds the important privacy qualifier. Every word earns its place, and no redundant detail is present.

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 simple one-parameter read tool without an output schema, the description provides enough context to select and invoke the tool: it identifies the resource, the public scope, and the key privacy exclusion. It does not enumerate return fields, but that is a minor gap for a standard profile lookup task.

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% and the single user_id parameter is already described as '사용자 UUID' in the schema. The tool description adds no additional parameter meaning, so the baseline score of 3 is appropriate.

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 states a clear operation ('retrieves') and a clear resource ('public user profile'), and further narrows scope by excluding personal information such as email. This is specific enough to distinguish it from the sibling get_* tools without requiring schema inspection.

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 gives no guidance on when to use this tool versus alternatives. There is no mention of when to prefer get_user_profile over get_feed, get_post, or other profile-related operations, so the agent must infer usage purely from the tool name and context.

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 provided, the description carries the full burden, and it does clearly convey a read-only, side-effect-free operation by saying it 'returns' data. It also discloses the key behavioral output: the category list and per-category post counts. It does not mention ordering, pagination, or whether empty categories are included, but for a simple no-parameter listing tool this is a minor gap.

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 single, focused sentence that states the resource and the returned data without any filler. It is front-loaded with the core purpose and includes the useful count detail without expanding into unnecessary explanation.

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 low-complexity, no-input tool with no output schema, the description provides enough information to understand what will be returned: categories and their post counts. It is slightly incomplete in not addressing ordering, pagination, or whether the counts are live, but these are not critical for an initial call to this kind of endpoint.

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?

The tool takes zero parameters and the input schema is empty, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because there is nothing ambiguous about inputs, and the description does not need to compensate for any schema gaps.

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 identifies the operation as returning the category list of Jeju-doldam and includes the additional detail of post counts per category. This is specific enough to distinguish it from sibling tools like get_post, get_feed, and search_posts, which deal with individual posts or feeds rather than category metadata.

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 implies usage when an agent needs to discover available categories and their post counts, but it does not explicitly state when to use this tool versus alternatives. It provides no exclusions or comparative guidance against sibling tools, so the agent must infer the appropriate context from the name and description.

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?

Since no annotations are provided, the description carries the burden of behavioral disclosure. It describes the return contents and the intended preflight role, and the list/return framing strongly implies a read-only operation. It does not explicitly state auth needs, rate limits, or absence of side effects, but for this simple list tool the disclosure is adequate.

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 two sentences with no filler. The first sentence states the return value and included fields; the second gives the concrete use case. Information is front-loaded and every sentence earns its place.

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 zero-parameter list operation with no output schema, the description is complete: it states what is returned, which fields matter, and when to call it. The explicit field names compensate well for the absence of an output schema.

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?

The tool has zero parameters, so the baseline of 4 applies. There are no parameter semantics for the description to explain, and the empty schema already fully covers the parameter surface.

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 states a specific verb and resource: it returns a list of persona accounts by category, and explicitly names the included fields (posted_today, category). This clearly distinguishes it from sibling tools like list_categories and post_as_persona.

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 provides a concrete workflow instruction: call this before post_as_persona to identify which personas have not posted today. This is clear context, though it does not mention when not to use the tool or name alternative tools beyond post_as_persona.

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 provided, the description carries the full disclosure burden. It does disclose two meaningful behavioral traits: admin authorization is required and the tool is reserved exclusively for automated (Claude cowork) posting, both of which gate whether an agent may invoke it. What is missing is the outcome of a successful call — whether the post is immediately published, rate limits, or error behavior — so the disclosure is adequate but not rich.

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?

Three short sentences, each carrying a distinct piece of information: the action, the prerequisite/discovery step, and the exclusive use case. It loses a point for minor redundancy — the persona email format and the category-UUID lookup source are already documented in the schema property descriptions, so those fragments add no new information.

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 four-parameter write tool with no annotations and no output schema, the description covers the essentials: the action, the permission requirement, the parameter-value discovery path, and the exclusive usage scope. The main gap is that it never describes the expected result of a successful call, and with no output schema there is nothing else to fill that in — but an agent has everything needed to select and invoke the tool correctly.

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%, so the baseline is 3, but the description adds genuine value beyond the schema by prescribing the discovery workflow: use list_personas to obtain both the persona email and the category UUID before calling. This materially improves the probability of a valid invocation, justifying a 4.

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 states a specific verb ('작성합니다' — creates/writes), a clear resource (게시글 — post), and the defining scope: posting as a specific persona account (persona.xxx@jejusns.com). It also distinguishes itself from siblings by declaring it an auto-posting-only (자동 포스팅 전용) tool, so an agent can tell it apart from create_post without opening the schema.

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 gives concrete prerequisites — 'admin 권한이 필요합니다' (admin permission required) — and an explicit discovery step: run list_personas first to obtain persona emails and category UUIDs. It also scopes usage to automated Claude cowork posting. However, it never names create_post as the alternative for manual or non-persona posting, so the when-not-to-use guidance is implicit rather than explicit.

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 the burden of behavioral disclosure. It adds useful semantics: wages are in KRW, interpreted together with wage_type, and undisclosed wages use wage_type='협의'. It does not mention read-only behavior, response shape, pagination, or default filtering of closed postings, but this is a reasonable disclosure for a search 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?

Two compact sentences: the first states the purpose and main filters, the second clarifies the non-obvious wage semantics. No redundant words, no repetition of schema details already present, and all content earns its place.

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 6-parameter tool with no annotations and no output schema, the description covers the core purpose and wage semantics but leaves gaps: it does not explain how region matching works, the role of limit, or the default behavior regarding closed postings. It is adequate but not fully complete for an agent operating without supplemental context.

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 67%, so the baseline is 3. The description goes beyond the schema by explaining wage_min's currency unit, the interpretation of wage_type, and the special '협의' value for undisclosed wages. This adds real semantic value that the enum and property names alone do not convey.

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 uses a specific verb ('searches') with a clear resource ('structured Jeju job postings') and specifies supported filters (region, occupation, wage). This distinguishes it from siblings like search_posts and search_property_listings even without naming them.

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 intended use is implied by the scope ('제주 채용 공고' = Jeju job postings), which suggests this is for job listings only. However, it does not explicitly state when to prefer this tool over search_posts or search_property_listings, nor provide exclusions or alternative routing.

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?

There are no annotations, so the description carries the full burden of behavioral disclosure. It usefully discloses that space-separated words are combined with AND and that category accepts either a Korean name or UUID. The verb '검색' implies a non-mutating read operation, but the description does not mention result ordering, whether matching applies to title/content/both, or pagination behavior beyond the schema's limit parameter.

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?

Two sentences with no filler. The first sentence front-loads the core purpose, and the second packs two useful semantic details (AND behavior and category format). It is appropriately concise for a simple search tool.

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 straightforward search tool with 3 simple parameters and no output schema, the description is nearly complete: it specifies search target, query combination behavior, category format, and the schema covers limit. The only minor gap is not explaining what the returned results look like or how they are ordered, but this is not critical for a search action.

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%, so the baseline is 3. The description adds value by explaining that multiple whitespace-separated words in the query parameter are all required (AND semantics), which is not stated in the schema. The category-as-name-or-UUID detail is redundant with the schema but reinforces expected usage.

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 a specific verb and resource: 'Search Jeju Dol-dam posts by keyword.' It is distinct from sibling search tools like search_jobs and search_property_listings because of the explicit '제주돌담 게시글' scope, and from get_post/get_feed through the keyword-search action. However, it does not explicitly name any sibling to differentiate itself.

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 gives no guidance on when to choose this tool over alternatives. It does not mention that get_post is for retrieving a single post or get_feed for browsing posts, nor does it state any exclusions. The AND semantics and category format are about how to use parameters, not about when to use the tool.

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 behavioral disclosure burden. It reveals non-obvious behavior: amounts must be in KRW with a concrete example (3억 → 300000000), region values must use Korean names, and Jeju-specific annual rent maps to yearly_max. It does not describe result ordering, pagination, or expired-listing defaults, but the core search behavior is transparent and side-effect-free.

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?

Three compact Korean sentences with no filler. The main action and filter scope are front-loaded, followed by the critical formatting and parameter-mapping conventions. Every sentence adds information that reduces the chance of incorrect invocation.

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 13 parameters, no annotations, and no output schema, the description targets the highest-risk ambiguities for Jeju property search: currency units, locality names, and the distinction between monthly and yearly rent. Combined with the schema's parameter descriptions and enums, this is sufficient for correct invocation, though explicit notes on default sort/limit and expired-listing behavior would make it fully 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 69%, so the description is not the sole source of parameter meaning, but it adds high-value semantics beyond the schema: currency unit, KRW conversion example, Korean region-name convention, and explicit mapping of monthly_max/yearly_max to 월세/연세. Uncovered details like sort and limit defaults are largely self-explanatory, and enums document their own allowed values.

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 verb ('검색합니다') and a concrete resource ('구조화된 제주 부동산 매물'), then enumerates the supported filter dimensions (지역·매물유형·거래유형·가격·면적). This clearly distinguishes it from sibling tools like search_posts/search_jobs and from the write-oriented upsert_property_listing.

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 gives clear, actionable context: it states the filter capability, provides currency formatting rules, specifies Korean region-name input, and explicitly disambiguates when to use monthly_max versus yearly_max. It does not name alternative tools or state exclusion conditions, but the search context and parameter guidance are clear enough for correct use.

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 provided, the description carries the full burden of behavioral disclosure. It does add meaningful context: admin-only access, required call ordering after post_as_persona, and the salary-privacy encoding rule. However, it does not disclose upsert semantics, whether existing records are overwritten, authentication requirements beyond 'admin', or any side effects, leaving notable behavioral gaps.

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 two sentences with no filler. It front-loads the admin constraint, then gives the sequencing instruction, then the key salary rule. Every sentence earns its place and the structure is easy to parse quickly.

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 14-parameter mutation tool with no annotations and no output schema, this description is not complete enough. It provides valuable orientation but omits upsert behavior, parameter semantics for most fields, what happens on conflict, and any expected response. An agent has enough to attempt the call, but not enough to invoke it confidently and safely across realistic variations.

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 only 29%, so the description must compensate for undocumented parameters. It adds a valuable rule for wage_type, wage_min_krw, and wage_max_krw (KRW integer, use 협의 and omit amounts when private), but most of the 14 parameters remain unexplained, including post_id provenance, source_name/source_ref/source_url, and contact fields. The compensation is too partial for the low schema 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 states a specific action ('저장합니다' / saves) on a specific resource ('채용 게시글' / job posting structured data), which clearly distinguishes it from sibling upsert_property_listing. It does not explicitly explain the 'upsert' (create-or-update) behavior, but the tool name and resource make the purpose reasonably clear.

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 gives a clear usage context: it is admin-only and should be called right after post_as_persona. This is actionable sequencing and access guidance. It does not explicitly state when not to use it or name alternatives, but the job-listing scope makes the intended use unambiguous.

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 the behavioral disclosure burden. It adds useful data-handling commitments (KRW integers with a conversion example; source_url is internal and never public), but it omits upsert semantics (what happens when post_id already exists), authorization failure behavior, and any side effects beyond saving.

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?

Three sentences in Korean, each carrying a distinct piece of information: purpose/scope, invocation timing, and shared field semantics. No filler or repetition.

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 definition covers the essential workflow and the two most important behavioral conventions (currency units and private field), which is enough for a routine follow-up call. However, for a write tool with 17 parameters and no output schema/annotations, it should also state upsert conflict behavior, applicable fields per deal_type, and what the tool returns; these gaps keep it at minimum viable.

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 65%, and the description adds cross-parameter meaning: all money fields must be KRW integers (7,300만원 → 73000000) and source_url is private. It also ties parameter usage to 'same fields' as post_as_persona, which helps the agent populate fields; however, a few schema-undocumented fields (floor, rooms, building_name, contact_phone) remain unexplained.

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 ('저장합니다' / saves), a clear resource ('부동산 게시글의 구조화 데이터' / structured real-estate post data), and an admin scope. It is clearly distinguishable from the sibling search_property_listings because it writes rather than reads, and the pipeline mention of post_as_persona reinforces the intended action.

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?

Explicitly tells the agent to call this immediately after post_as_persona and send the same fields, which is a concrete when-to-use condition. It also notes admin-only access, but it does not enumerate when-not-to-use or point to alternatives such as upsert_job_listing.

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