Urlaub Smart

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

No annotations are provided, so the description must carry the burden of disclosing behavioral traits. It mentions the output (markdown table, page URL) but does not state whether the tool is read-only, requires authentication, or has any side effects. This is a moderate gap given that the tool likely performs a read-only query.

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 sentence that efficiently conveys the core function and output, with examples embedded. It is slightly dense but remains focused and front-loaded with the main action ('six best non-overlapping vacation levers'), earning a high score for conciseness.

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 detailed schema and simple input requirements, the description provides sufficient context for an agent to understand the tool's purpose and output. It does not mention edge cases or preconditions, but the schema plus description cover what is needed for typical use.

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 descriptions cover all three parameters (year, locale, region) with clear explanations (e.g., region format examples). The tool description itself adds no further meaning beyond the schema, so the baseline 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 clearly states the tool's function: it provides the six best non-overlapping vacation levers for a region and year, specifying the types (classic bridge days, holiday clusters) and output details (booking dates, holidays used, factor, markdown table, page URL). This leaves no ambiguity about what the tool does.

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 does not explicitly differentiate this tool from its siblings (list_regions, plan_vacation, public_and_school_holidays). While the purpose implies when it might be used (e.g., for vacation planning), it lacks an explicit statement about when to prefer this tool over alternatives or when it is not appropriate.

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. It does not explicitly state that the tool is read-only or has no side effects, though the nature of a reference list implies safety. This lack of explicit transparency prevents a higher score.

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, compact sentence that conveys all essential information without any redundancy or 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, the description must explain the return content. It lists the key fields (codes, names, slugs) and scope (countries and regions), which is adequate for a simple listing tool. It does not specify the exact structure (e.g., array of objects), but this is not critical for basic usage.

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. The description clearly explains what data is returned (countries, regions, codes, names, slugs), which covers the expected output without needing parameter documentation.

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 title clearly states 'List supported countries and regions', but the description itself is a noun phrase rather than an explicit verb phrase. Still, it conveys the purpose of enumerating reference data and is distinct from sibling tools.

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 phrase 'accepted by the other tools' implies that this tool provides valid values for use in other tools, giving clear context for when to invoke it. It does not explicitly state when not to use it, but the references to sibling tools provide sufficient guidance.

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

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

No annotations are provided, so the description carries the full burden. It discloses the output structure (plan blocks, opportunities, markdown table, URL) and includes the instruction to cite the URL. This gives a clear picture of what the tool returns without requiring assumptions about side effects.

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 concise and well-structured, front-loading the core purpose and then summarizing the return value. It avoids unnecessary fluff while covering all essential information, making it easy for an agent to grasp 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?

Despite lacking an output schema, the description sufficiently explains what the tool returns, including the components of the plan and the URL instruction. Combined with complete parameter descriptions, an agent has enough context to invoke the tool correctly and interpret the response.

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 each parameter has a meaningful description (e.g., carryoverDays explains expiry and priority, companyClosures explained as inclusive ranges). The descriptions add context beyond the schema, such as the meaning of 'balanced' strategy and how schoolHolidays affect booking.

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's purpose: planning vacation days optimally for a region and year to maximize holiday blocks. It uses specific verbs like 'plan' and 'returns' and distinguishes itself from siblings like best_bridge_days or list_regions by focusing on the complete vacation plan.

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 makes the tool's function explicit, but does not directly contrast it with siblings. An agent can infer that this tool is for full vacation planning, while siblings handle individual aspects (bridge days, regions, holidays). Clear context but lacking explicit 'use this when' guidance.

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 of explaining behavior. It details the included data (weekday, national/regional scope, weekend flag, school holiday periods) and the source, but does not mention error handling, potential limits, pagination, or any side effects. For a simple read operation, this is adequate 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 a single, well-structured sentence that front-loads the key output (all public holidays and school holiday periods) and includes clarifying attributes in parentheses. It is concise and free of unnecessary detail.

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 and lack of an output schema, the description sufficiently conveys what the response contains (attribute details for holidays and school periods) and identifies the data source. It does not describe the output format, but for this use case the description provides enough context for an agent to understand what to expect.

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 covers 100% of the parameters with descriptions, including examples for region and defaults for year/locale. The tool description itself does not add additional parameter semantics beyond the schema, 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?

The description clearly states the tool's purpose: returning all public holidays (with specified attributes) and school holiday periods for a given region and year. It identifies the specific resource and scope, distinguishing it from siblings like best_bridge_days (which compute bridge days) and plan_vacation (which suggests vacation plans).

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 that this tool is for retrieving raw holiday data for a region and year, but it does not explicitly state when to use it versus siblings such as best_bridge_days or plan_vacation. No direct mention of alternatives or exclusions is provided, though the tool's specificity makes the use case reasonably clear.

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