Hotel Bergsonne Allgäu Booking

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

With no annotations at all, the description carries the full behavioral burden and does so: it discloses that unpriced stays are never confirmed at 0.00 EUR, that cash is not accepted, that payment is via a secure link requested after submission and due 7 days before arrival, and that city tax is pre-included. These are exactly the operational constraints an agent needs before committing a booking.

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-loads the verb and resource, then layers prerequisites, validation behavior, payment terms, and the sandbox flag. It is dense but every clause earns its place; the only cost is a somewhat long single block of prose with no visual separation.

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 mutation tool with no annotations and no output schema, the description covers prerequisites, rejection semantics, sandbox mode, pricing rules, and payment flow comprehensively. The one omission is what a successful call returns (confirmation identifier, status, next steps), though a separate idempotencyKey in the schema hints at retry semantics.

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 the baseline is 3; the description goes slightly beyond by giving concrete roomId examples (KAPDZ, KDZ, KFZ, SDZ), restating the required guest-data set, tying addonId to the get_addons catalog, and clarifying the pricing rule that Kurtaxe must not be added on top. It adds modest value over the already-complete schema.

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 ('Create a hotel reservation at Hotel Bergsonne Allgäu') and immediately frames it against the read-only siblings that must be called first. An agent can distinguish this write tool from search_availability/get_price_quote without opening any schema.

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?

Gives explicit prerequisites ('Call search_availability and get_price_quote for the same dates and room first'), the failure modes when those prerequisites are violated (NOT_AVAILABLE, INVALID_RATE), and the sandbox alternative via test: true. It also states when payment happens, so the agent knows this call does not collect payment.

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 burden; it does disclose the returned fields (addonId, name, type, priceType, unitAmount), which is valuable given there is no output schema. However, it says nothing about read-only/permission requirements, pagination, catalog size, or empty-result behavior, so the behavioral picture is only partially filled in.

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 tight sentences, front-loaded with purpose, then the return shape, then the downstream usage. No filler or repetition of the name.

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 zero-parameter read tool with no output schema, the description covers what it returns and how the result is used. Only minor gaps remain (no note on whether the whole catalog is returned at once or any rate/size constraints), which are low-impact here.

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 takes zero parameters, so per the rubric the baseline is 4. The description appropriately does not invent parameter semantics and instead explains how the returned addonId is consumed.

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 ('Fetch the catalog of bookable add-on extras') and grounds it with concrete examples (wine, flowers, activities). It also names the sibling it feeds into (create_booking), so an agent can distinguish it from get_fact_sheet, get_price_quote, and the other siblings.

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?

Gives clear context: it is the catalog lookup whose addonId values feed create_booking, which tells the agent where this sits in a booking flow. It does not name explicit alternatives or when-not-to-use conditions, so it falls short of 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses payload contents and the German-only locale constraint, but says nothing about authentication, rate limits, caching, or error behavior for what is presumably an external GIATA 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?

Three short sentences, front-loaded with the verb and resource, then payload contents, then locale and disambiguation. The embedded specific hotel name ('Hotel Bergsonne Allgäu') is slightly unusual for a generic tool but does not waste words.

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 zero-parameter tool with no output schema and no annotations, the description covers what comes back, the language constraint, and how it differs from operational hotel data. Enough for an agent to decide to call it and expect a usable result.

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 takes zero parameters, so there are no parameter semantics to explain; the description is not deficient here. Baseline of 4 applies for a parameterless tool.

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 ('Fetch') and resource ('GIATA property fact sheet') and enumerates the payload it returns (descriptions, amenities, images, room classifications). It also implicitly separates itself from the operational-details sibling by labeling this as marketing/classification data, though it does not name get_hotel_details directly.

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 rather than stated: 'separate from operational hotel details' hints that get_hotel_details should be used for operational data, but no explicit when-to-use/when-not-to-use rule or alternative is named. The 'German content only' note is useful scoping but not routing 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. It discloses the returned data shape (useful since there is no output schema) and 'Fetch' implies a read, but it is silent on auth/scope requirements, whether the call targets a specific hotel or an ambient WBE session, and any rate or error behavior. Minimum viable, with real 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?

Two sentences, zero waste. The core payload contents lead and the sibling disambiguation is placed second, which is the right ordering for an agent scanning for routing cues.

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 no-param, no-output-schema tool the description adequately explains what the caller receives, which is the main completeness gap a missing output schema creates. It does not address how the hotel is identified given there are no parameters, which is a minor but genuine open question.

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 takes zero parameters, so the baseline is 4 and there is no schema semantics the description could be duplicating. It does add vocabulary for returned fields (e.g. roomId abbreviation codes), but that is output naming rather than parameter guidance.

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 ('Fetch') and resource ('hotel operational data from the WBE system'), then enumerates exactly what comes back (room types with roomId codes, check-in/out times, contacts). It also distinguishes itself from the sibling get_fact_sheet, so an agent can route correctly without opening another schema.

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?

Explicitly names the alternative and the condition that selects it: 'For rich marketing descriptions, use get_fact_sheet instead.' The when-to-use case (operational data) and the when-to-use-something-else case are both stated rather than 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?

No annotations are provided, so the description carries the full behavioral burden. It does add genuinely useful domain context (city tax of 3.50 EUR/person/night already included in rates, preventing a wrong interpretation of the total), but it says nothing about whether the quote is binding, what currency/response shape to expect, or any prerequisites beyond the roomId source.

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 short sentences, each earning its place: the capability, the required identifier convention, and the tax caveat. Front-loaded with the core purpose and no 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 does describe the return ('nightly price breakdown and total'), and the tax note clarifies the number's composition. It is nearly complete for a read-only quote tool, though it omits date format expectations and error/edge behavior.

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 all four parameters are already documented in the schema; the description's roomId note duplicates the schema's own roomId description almost verbatim. It adds no syntax or format detail (e.g. date format, whether adults defaults to 1) beyond what the schema already supplies.

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 ('Get') and resource ('nightly price breakdown and total') scoped to a specific room and property. It also implicitly separates itself from search_availability by naming it as the source of roomId rather than a competing tool.

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 establishes the workflow dependency: roomId must come from search_availability results, telling the agent this tool is invoked after a search. It does not state when not to use it or what to do if the room is unavailable, so it stops short of explicit alternatives/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, the description carries the full burden, and it does add genuine behavioral context: the return shape (dates with rates plus a roomTypes array) and the material pricing fact that the 3.50 EUR/person/night Kurtaxe is already baked into all rates. It omits auth/permission requirements and any rate-limit or caching caveats, so it is strong but not complete.

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 tight sentences, front-loaded with the core purpose, then output shape, then the cross-tool usage hint and the pricing caveat. No sentence is redundant.

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?

No output schema exists, so the description correctly compensates by naming the returned structures (dates/rates, roomTypes, sample roomIds) and the tax-inclusive pricing. For a 4-param read tool with no annotations it is nearly complete, lacking only auth or error-handling context.

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 all four parameters (adults, checkIn, checkOut, children) are already documented including accepted date formats. The description adds nothing about parameter behavior beyond naming room IDs that come from the output, so the baseline 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 and resource ('Search real-time room availability and daily pricing') scoped to a named hotel, and distinguishes itself from siblings by naming get_price_quote and create_booking as the downstream consumers of its roomId output.

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 establishes this as the discovery step in a workflow: 'Use the roomId from roomTypes ... for get_price_quote and create_booking.' That tells the agent when to reach for this tool relative to siblings, but it never states exclusions or what to do when availability is empty (e.g. fallback to get_fact_sheet).

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