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?

With no annotations, the description carries the full burden. It discloses the nature of the data ('field-verified', 'no resellers', 'honest local notes') which provides context on data quality and tone, but doesn't discuss operational behavior such as response format, data freshness, or potential limitations. This is adequate but not comprehensive.

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 clear structure: the first enumerates content areas, the second gives usage examples. No redundant phrases; every sentence serves a distinct purpose.

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 lookup tool with one well-documented parameter and no output schema, the description sufficiently communicates scope and typical use cases. It would benefit from a note on the return format, but overall it's complete for an agent to invoke 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 schema already provides 100% coverage for the single 'city' parameter with an example. The description repeats the placeholder without adding new semantic details, so it doesn't exceed the baseline for high 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 tool name 'get_city_facts' combined with the description explicitly lists the specific facts a foreign traveler needs (attraction booking, hotels, foreign cards, local notes), using a clear verb+resource construction. It distinguishes itself from siblings like check_attraction_booking by covering a comprehensive city-level overview rather than a single attraction.

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 states 'Use for questions like...' providing concrete example queries, which gives clear when-to-use guidance. It doesn't explicitly name alternatives or exclusions, but the examples naturally differentiate from sibling tools focused on specific attractions or transit.

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 must carry the full burden of behavioral disclosure. It explains the content scope and gives example queries, but it does not mention the output format (e.g., whether it returns a list, paragraphs, or structured data) or any other behavioral details. For a read-only informational tool, this is acceptable but not exhaustive.

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: the first establishes the tool's purpose and scope, the second provides concrete use cases. While the list of topics is long, it is necessary to convey breadth. No filler or redundancy, though it could be slightly more compact.

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 adequately covers what the tool is for and when to use it. It doesn't spell out the return structure, but for a playbook-style reference this is a minor gap. Sibling differentiation is clear enough, and the example queries enhance completeness.

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 describes the 'topic' parameter with an enum, so coverage is 100%. The description adds value by pairing each enum value with real-world example queries (e.g., payments → 'card declining', connectivity → 'VPN or eSIM'), enriching the semantic meaning beyond the bare enum labels.

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 TravelerLocal's field-verified guidance on systemic challenges foreigners face in China. It explicitly distinguishes itself from place-specific tools by noting 'not a specific place' and itemizes its exact topics (payments, connectivity, phone number, arrival, hotels, transit), making it easy to differentiate from siblings like get_city_facts or check_transit_eligibility.

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 offers explicit usage examples ('Use for "why does my card keep getting declined in China"', etc.), which indicates when to invoke the tool. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of a perfect score.

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 burden. It discloses that the search is limited to field-verified pages and that results include city names and URLs, providing useful behavioral context. However, it does not mention read-only behavior, sorting, or limitations, so it is not fully 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 two sentences, front-loading the action and resource in the first sentence and usage in the second. No word is wasted, and it is appropriately sized for such a simple 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 single-parameter search tool with no output schema, the description covers the purpose, output contents, and usage scenarios. It lacks details like result limits or ordering, but for this simple tool, it is largely 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 input schema already provides 100% coverage with a clear description of the `query` parameter ('Partial city name or keyword'). The tool description does not add additional parameter semantics beyond rephrasing the matching behavior, so it meets the baseline without extra value.

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 the specific verb 'Search' with the resource 'cities TravelerLocal has field-verified pages for', clearly distinguishing it from sibling tools like check_attraction_booking or get_city_facts. It also states the return value (city names and page URLs), making 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?

The second sentence explicitly provides two use cases: 'discover coverage or resolve a place to a city'. This gives clear context for when to use the tool, though it does not name alternative tools or exclude other scenarios.

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