Mink Viking Experience

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

Even though annotations already mark this as read-only, idempotent, and non-destructive, the description adds substantial behavioral context: empty list means fully booked (not an error), prices are in EUR with optional secondary currency with fallback behavior, and the structured response always contains full multi-currency breakdown. This goes beyond the annotations and informs the agent about expected edge-case behavior.

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-loaded with the core purpose, and every sentence offers unique value—real-time checking, return content, empty-list semantics, and currency behavior. No filler or repetition of schema content.

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 burden of explaining what the tool returns. It covers available start times, session duration, pre-filled booking URL, pricing, and the meaning of an empty list, all critical to using the tool correctly. It also addresses currency handling. For a read-only tool with detailed input schema, this is comprehensive.

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 does not add parameter-specific meaning beyond what the schema already provides; for instance, the schema already documents currency fallback and participants range. The tool description mentions pricing and secondary currency but mirrors schema details without adding new parameter semantics.

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 real-time availability for a specific date, with a specific verb and resource. It distinguishes itself from siblings that find next available or fetch booking URLs by emphasizing date-specific lookup and immediate return of available times, duration, booking URL, and pricing.

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 clearly implies when to use this tool (for a specific date availability check) and even notes that an empty slot list means fully booked, which helps set expectations. However, it does not explicitly name alternatives like find_next_available or get_booking_url or state when not to use it, so it misses the explicit exclusion guidance that would earn 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?

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the behavioral detail that it scans a range and returns the first available dates, but this is largely inferable from the name and schema (e.g., max_dates, days_ahead). It does not disclose additional behaviors such as how availability is determined or whether pricing is included in the response.

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 sentences with no wasted words. The first sentence states the action and output, and the second provides usage context. It 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?

The tool has no output schema, so the description should summarize return values; it does so by stating it returns the first N dates with available slots. The schema covers all parameter semantics, and annotations cover safety. However, the description does not explicitly mention that the output includes pricing (hinted by the currency parameter) or how availability is determined in relation to participants, which are minor gaps but not critical given the schema's detail.

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 every parameter having a clear description in the input schema. The main description does not add any parameter-level meaning beyond what the schema already provides. The baseline of 3 is appropriate because the schema does the heavy lifting and the description offers no additional semantic detail.

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: 'Scan upcoming days and return the first N dates that have available slots.' This is a specific verb (scan) with a clear resource (upcoming days) and output (dates with available slots). It also distinguishes the tool from siblings (e.g., check_availability) by describing a flexible date search, and includes intended use cases ('no specific date in mind' or 'requested date is fully booked').

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 guidance on when to use this tool: 'Use this when a visitor has no specific date in mind or when a requested date is fully booked.' This clearly sets the context for use, but it does not explicitly mention alternatives or exclusions (e.g., 'for checking a specific date, use check_availability instead'), so it lacks a full when-not-to-use clause.

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, and destructiveHint=false, so the safety profile is covered. The description adds useful context by naming the target URL (mink.is/booknow) and clarifying that this is a handoff, not a booking creation. However, it does not describe the return format or behavior for invalid inputs beyond what the schema implies.

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-loaded with the action and target, and contains no filler. Every word contributes to understanding what the tool does and when to use it.

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 two-parameter tool with strong annotations and clear schema, the description is nearly complete: it covers purpose, usage timing, and key parameter roles. A slight gap is the lack of an explicit return value description, though 'Generate a pre-filled booking URL' makes the output obvious.

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 50%, with 'date' documented and 'participants' only having type/range/default. The description adds minimal semantic value by mentioning 'participant count' and confirming both values get embedded in the URL, but it does not elaborate on participants beyond what the schema already 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 clearly states the tool's function: 'Generate a pre-filled booking URL that opens mink.is/booknow with the date and participant count already selected.' It identifies a specific verb, resource, and output, and distinguishes itself from sibling tools like check_availability and get_pricing by positioning itself as the final checkout handoff step.

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 clear usage context: 'Use this as the final step in a booking conversation to hand the visitor off to checkout.' This tells the agent when to invoke it, though it does not explicitly mention when not to use it or name alternative 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds a list of return fields, which is informative but does not disclose additional behavioral traits like error cases, data freshness, or authorization needs. This is acceptable given the strong annotations.

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 lists the key return values in a clear, logical order. Every word contributes to the meaning, and it is front-loaded with the verb 'Return' and the specific business entity.

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 parameters, the description provides all necessary information about what the tool returns. It covers all relevant data points (hours, address, coordinates, contact, booking policy) and is complete for the tool's simplicity.

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?

There are zero parameters, and the schema is an empty object with 100% coverage. The description does not need to explain parameters. The baseline for 0 params is 4, and the description appropriately focuses on return content rather than inputs.

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 specifies the action (Return) and the exact resources (operating hours, street address, GPS coordinates, phone, email, booking policy) for a specific business. This distinguishes it from sibling tools like check_availability and get_pricing which target different concerns.

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 context by listing specific information types, but it does not explicitly state when to use this tool versus alternatives like check_availability or get_booking_url. There is no mention of exclusions or comparison with siblings, so the guidance is only implicit.

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 the operation read-only, non-destructive, and idempotent. The description adds useful context by explaining that rates come live from the shop's multi-currency exchange-rate cache (WCML), which signals data freshness and source. It does not contradict the annotations.

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-loaded with the core purpose, and every clause adds value. It avoids unnecessary repetition of schema or annotation 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 simple read-only lookup with one optional parameter and no output schema, the description adequately enumerates what the agent can expect (per-person rates, print pricing, gift card validity, session inclusions). It also notes the live-cache source, making the tool's behavior sufficiently transparent for 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 input schema fully documents the optional currency parameter with pattern, examples, default, and fallback behavior. The description adds minimal parameter-specific meaning beyond mentioning multi-currency, so a baseline 3 is appropriate given the 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 description opens with a specific verb ('Return current pricing') and identifies the exact resource ('Mink Viking Experience'), listing the included data categories. This clearly distinguishes it from sibling tools like get_business_info or check_availability.

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 clearly implies this tool is for retrieving current pricing details, which provides clear context for when to use it. It does not explicitly mention when not to use it or compare it to sibling tools, but the context is unambiguous enough for an agent to select it for pricing requests.

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