AIFinder

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered without the description. The description adds real behavioral context beyond that: the result is capped at 10 services and is scoped to exactly one category. It does not describe the shape of the comparison output, which is the main remaining 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 with the operation and scope front-loaded; nothing is padded. The Korean sentence is a direct translation of the English one, which doubles length without adding information, though it is defensible for a Korean-market 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?

With no output schema, the description usefully states what the comparison covers (Korean UI, KRW pricing, starting price, trial conditions), which is exactly what an agent needs to decide whether this satisfies a request. Only the result structure beyond those dimensions is unaddressed, a minor gap for a read-only 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 coverage is 67%: category is documented as a slug pointing at list_categories, but sort only defines its 'korean' value and leaves 'popular' and 'pricing' unexplained, and limit has no prose. The description contributes the 10-service ceiling (matching limit's maximum) and confirms the single-category constraint, but does not clarify the unresolved sort enum 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?

Names a specific verb (compare) and resource (top AI services in one secondary category) and enumerates the comparison dimensions (Korean UI, KRW pricing, starting price, trial conditions). The 'compare' framing naturally separates it from search_ai_services and get_ai_service, but it never names a sibling or states what it is not, so differentiation is inferred rather than explicit.

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?

Usage is implied: reach for this when you need a side-by-side comparison within a single secondary category. There is no explicit when-not guidance and no mention of find_alternatives or search_ai_services as alternatives for adjacent needs, leaving the agent to infer the boundary.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description usefully adds that ranking comes from a rule-based score excluding ads and ratings, and caps results at 5, but says nothing about ordering stability, missing-key behavior, or what a 'verified Korean UI' means operationally.

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 English portion is two front-loaded sentences with direct value (ranking method, result cap). The Korean sentence is a full duplicate that adds length without new information, keeping it out of 5 territory.

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 read-only lookup with no output schema and four annotations, the description gives the essential framing: scope, ranking basis, and result count. Missing detail about the returned fields is acceptable since no output schema exists to define a contract.

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 67%: 'key' and 'korean_ui_only' are documented, while 'limit' has only bounds and a default. The description's 'Up to 5 results' restates the schema's maximum rather than explaining how limit interacts with ranking. Baseline 3 is appropriate given the schema does most of the work.

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 ('Find alternatives to an AI service') plus a scoping constraint ('within the same category'), which is enough to distinguish it from get_ai_service and search_ai_services. It does not explicitly contrast itself with the closest sibling, compare_category, so it falls short of a 5.

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 the use case (given one service, find peers), but never states when to prefer it over compare_category or search_ai_services, nor any prerequisite such as needing a key from prior search results. Usage is inferable but not guided.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context beyond that: it discloses the content and character of the result (verified Korean support with plain-language evidence, trial conditions) and the key-vs-URL fallback for lookup.

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 English sentence is front-loaded with the verb and the returned fields, and the second sentence is short. The Korean restatement duplicates the same content rather than adding information, a minor redundancy cost.

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, the description carries the return-value burden and does so by enumerating the result fields. Two optional parameters, read-only annotations, and an unambiguous single-service scope make this sufficient for correct invocation; only the key/URL precedence edge case is slightly under-explained.

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 both `key` and `url` are already documented, including the 'used when the key is unknown' fallback. The description restates this ('pass the key from search results or the service's official URL') without adding format or validation detail, so the baseline of 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+resource ('Get details of one AI service') and enumerates exactly what the payload contains: Korean description, verified Korean support and trial conditions, pricing, platforms, login methods, links. The scope word 'one' implicitly separates it from search_ai_services, but 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?

It establishes the pipeline context by telling the agent to 'pass the key from search results or the service's official URL', which signals that this tool is the follow-up to search_ai_services. No exclusions or comparison against find_alternatives / compare_category are given.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds value beyond that by disclosing the return shape (slug, Korean name, service count) — important since there is no output schema — and by naming the downstream consumer of the slugs.

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 content is front-loaded and only two sentences, but it is duplicated verbatim in English and Korean, doubling the token cost with no additional information for the agent.

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 read-only lookup with no output schema, the description supplies the returned fields and the downstream slug usage, which is nearly everything an agent needs. Only the `parent` filtering behavior is left solely to the 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?

With a single optional parameter at 100% schema description coverage, the schema fully documents `parent` as the subcategory filter. The description adds nothing about it, which is the expected baseline when the schema does the work.

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 (List) plus resource (AIFinder categories) and even enumerates the returned fields (slug, Korean name, service count). Among siblings like compare_category, find_alternatives, and search_ai_services, no other tool enumerates the category taxonomy, so an agent can route to it unambiguously.

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 tells the agent what to do with the output ('Use the slugs for the `category` argument of other tools'), which implies when to call it, but there is no explicit when-to-use or when-not statement and no named alternative. Usage must be inferred.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds value beyond them by disclosing result granularity ('up to 10 services per call') and data provenance (measurement dates, links, verification against each official site), which the annotations cannot convey.

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 core operation and scoped in two efficient sentences, with the Korean rendering adding local nuance rather than pure duplication. Slightly longer than strictly necessary, but every clause contributes scope, query-mode, or return-format 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?

With nine parameters, no required fields, no output schema, and rich annotations, the description covers purpose, query behavior, and return shape. It omits pagination behavior (page capped at 3), the meaning of the filter enums, and any routing guidance among the four sibling tools, leaving gaps an agent must fill elsewhere.

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 56%, so the schema documents roughly half the nine parameters (try, sort, query, korean, category). The description only adds the notion that queries accept natural-language Korean or English and that filters exist; it does not explain the pricing, platform, page, or limit semantics. Baseline 3 is appropriate when the schema carries part of the burden.

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 names a concrete verb and resource ('Search AI services listed on AIFinder') and goes further by characterizing the directory (Korean AI-tool directory with verified Korean support, KRW pricing, business registration, trials). An agent immediately knows this is the discovery/search tool for the service catalog, but no sibling is named to disambiguate from get_ai_service or find_alternatives.

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?

Usage is implied: 'Accepts natural-language Korean or English queries and filters' conveys that you can search by keyword or browse by filters. However, there is no explicit when-to-use guidance, no statement of when to prefer get_ai_service or find_alternatives, and no prerequisites or exclusions.

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