TravelerLocal — China travel facts for foreign visitors

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

No annotations are provided, so the description carries the burden. It discloses what information the tool returns (booking requirements, release times, sell-out status, official channel, resellers), which is helpful and gives expectations. However, it doesn't mention limitations like whether it covers all attractions, whether data is real-time, or any error conditions, so transparency 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?

The description is two sentences, with the first sentence front-loading the core value proposition and the second giving concrete usage examples. Every word contributes; no fluff 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?

Given it's a simple one-parameter check tool with no output schema, the description covers the key aspects: the input (attraction) and the nature of the output (booking-related details). It lacks generic disclaimers about data availability, but for a tool of this complexity it is sufficiently 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?

The schema description coverage is 100% (the 'attraction' parameter is described with an example). The tool description reinforces the example with Forbidden City etc., but adds no new semantic detail beyond what the schema already provides. Baseline 3 applies because the schema fully documents the parameter.

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 what the tool does: it provides information on how foreigners book specific Chinese attractions, covering real-name/passport requirements, ticket release time, sell-out status, official channels, and authorized resellers. It uses specific verbs and resource types, and the sibling tools (transit, city facts, travel playbook) are clearly distinct.

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 explicitly says 'Use for' and gives concrete example queries ('can foreigners book the Forbidden City / Terracotta Army / Mogao Caves and when do tickets release'), which clearly indicates when to invoke this tool. It doesn't explicitly state exclusions or alternatives, but the provided use cases make the intended context 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 bears full responsibility for behavioral disclosure. It states the tool 'Returns eligibility, regional limits, and the defining conditions,' which gives some output transparency, but it does not explicitly state that this is a read-only query, mention any side effects, rate limits, or error behavior. It adds moderate value but has 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 three sentences, front-loaded with the core purpose in the first sentence. It avoids fluff and clearly distinguishes the tool type, making it highly scannable and 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?

The description covers purpose, inputs, and outputs, but it says 'Needs nationality, the entry port/city, and whether they have an onward ticket,' implying all three are required, while the schema marks only nationality as required. This ambiguity about optional parameters is a gap. Additionally, there is no discussion of what happens when optional parameters are omitted, though the output description is helpful.

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%, so the baseline is 3. The description repeats the parameters ('nationality, the entry port/city, and whether they have an onward ticket') but adds no new semantic detail beyond the schema descriptions, which already explain the 'third country' condition. The description is consistent but not additive.

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 checks eligibility for China's 240-hour visa-free transit, explicitly distinguishing it from the 30-day visa-free entry. This provides a specific verb (check) and resource (transit eligibility), and the exclusion sets it apart from any similar concept.

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 context on what the tool does and explicitly excludes the 30-day visa-free entry, but it does not name alternative tools or provide when-not-to-use scenarios. The mention of required inputs (nationality, port, onward ticket) implies the typical use case, but explicit alternatives are missing.

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?

The description discloses key behavioral traits: data is 'checked against official sources and dated', indicating reliability and currency. It notes that ticket channels are 'official (no resellers)', which is a specific honesty indicator. With no annotations provided, the description carries the full burden, and it does so well by describing the source quality and exclusions. However, it doesn't mention whether the data is cached, how often it's updated, or what happens for unsupported cities, which would provide even fuller transparency.

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 paragraph with front-loaded purpose and detailed examples. Every sentence adds distinct value: the first sentence defines scope, the middle lists key data categories, and the last provides usage examples. No wasted words. It is efficient and easy for an AI 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?

Given the tool has only one parameter (city), no output schema, and no annotations, the description is remarkably complete. It covers the input parameter clearly, explains what data will be returned (with sufficient detail for an agent to judge relevance), and gives example queries. The sibling context makes its role in the tool ecosystem clear. There is no missing information that would prevent correct invocation or interpretation.

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 only parameter is 'city' with complete schema coverage. The description adds significant value beyond the schema by explaining what 'city' represents (a Chinese city for foreign traveler facts), giving examples ('"Beijing" or "lhasa"'), and showing how it's used in queries. The schema's description was brief, so the tool's description compensates richly. A small deduction for not explicitly saying the slug can be in Chinese characters (if applicable) or clarifying boundary cases.

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 what the tool does with a specific verb ('Facts') and resource ('a Chinese city'). It lists the exact categories of information provided (attractions, tickets, hotels, payment) and who it's for (foreign travelers). This distinguishes it effectively from siblings like 'check_attraction_booking' and 'check_transit_eligibility', which are narrower in scope.

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 explicitly tells when to use this tool: for questions like 'how do I book X in <city>' or 'can foreigners stay in hotels in <city>'. It also contrasts with siblings implicitly by covering a broad set of travel needs rather than a single check. The guidance is concrete and actionable for an AI agent deciding between tools.

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 exist, so the description carries full burden. It discloses that the response is 'TravelerLocal's stance, checked against official sources and dated', indicating reliability and timeliness. It does not reveal details about side effects or authentication needs, but for a read-only informational tool this is sufficient. No contradictions with annotations (none provided).

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 paragraph that front-loads the core purpose. It efficiently enumerates topics and example queries without redundancy. Minor room for shortening, but every sentence adds value. It is structured well for quick comprehension.

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 simplicity (one enum parameter, no output schema), the description is fully complete. It covers the tool's output (dated stance on systemic issues), the exact topics available, and example use cases. No missing critical details for an AI agent to select and invoke this 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?

The sole parameter topic is an enum with a schema description listing the six values. The description adds substantial meaning by mapping those values to real user questions (e.g., 'do I need a VPN or an eSIM' for connectivity) and explaining the context of each topic. This goes beyond the schema's brief value names, making it very helpful for an AI to select the correct parameter.

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 what the tool returns: 'TravelerLocal's stance... on the system-level things foreigners get wrong in China'. It lists specific subtopics (payments, connectivity, etc.) and differentiates from siblings like check_transit_eligibility or get_city_facts by focusing on systemic advice rather than specific queries or bookings.

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 explicit example queries (e.g., 'why does my card keep getting declined in China') that map to the tool's purpose. It implies when to use it—for common pitfalls—but does not explicitly contrast with sibling tools or state when not to use it. The context is clear but lacks direct exclusions.

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 full behavioral burden. It discloses the tool searches for cities that have guides, returns matches, and is used for discovery/resolution. However, it doesn't explain search behavior nuances (case sensitivity, partial match rules, ordering, or if no match is found). The description adds value but leaves some 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?

Three sentences, each serving a distinct purpose: what the tool does, what it returns, when to use it. Zero waste, perfectly front-loaded.

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 fuzzy search tool with one parameter, the description covers purpose, return format, and usage contexts. No output schema exists, but the return format (city names + URLs) is simply stated. Very minor gap: doesn't mention edge cases like no 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 schema already has 100% coverage (query described as 'Partial city name or keyword'), so the description's mention of 'matching city names and page URLs' adds context about the return, but doesn't enhance parameter understanding beyond what the schema provides. Baseline 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 specific verbs ('Search', 'Returns') and a clear resource ('cities TravelerLocal has guide pages for'), making the tool's purpose immediately obvious. It also distinguishes itself from siblings by focusing on city discovery rather than facts, booking, or transit checks.

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 explicit usage contexts: 'discover coverage' (finding which cities have guides) and 'resolve a place to a city' (mapping a place to a named city). It implicitly states when to use this tool (when you need to find a city or check coverage) without explicit negatives, but doesn't mention when not to use it (e.g., if you need city facts, use get_city_facts).

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