Bandago Van Rentals

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

With no annotations, the description carries the behavioral burden. It discloses that availability is computed by a scheduling engine that accounts for reservations, vehicle positioning, and fleet consolidation, and it states the return shape (rate codes with urgency labels). A minor gap is that the urgency labels are not defined.

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 concise sentences with no filler: the purpose is front-loaded, followed by algorithmic context and return behavior. Every sentence contributes 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 5-parameter tool with no output schema or annotations, the description covers purpose, computation context, and return content. It doesn't detail the meaning or range of urgency labels, but an agent has enough to select and call the 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?

Schema coverage is 100%, so each parameter already has a description. The tool description adds context around dates/locations but does not need to repeat parameter details; 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?

Description names a specific verb ('Check'), a resource ('vehicles available for rental'), and the key filtering dimensions (specific dates and locations). The return statement about available vehicle types/rate codes distinguishes it from siblings like get_rate_quote and list_rate_codes.

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 a clear use case: checking availability for rental on given dates and locations. It does not explicitly name sibling alternatives or exclusions (e.g., when to use get_rate_quote instead), so it falls short of full 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 transparency burden. It discloses that the tool 'Reads the centralized policy source of truth,' implying a non-mutating operation, and mentions state-correctness and restrictions. It does not explicitly state it makes no changes or discuss potential errors, but the read-only nature is clear from 'Reads' and 'Explain.'

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 somewhat long but every sentence adds unique value: product list, data sources, DWP detail, parameter usage, and explicit guidance to not rely on memory. It is structured logically from products to usage, but could be tightened by reducing redundancy (e.g., 'DWP' is mentioned with its full name and purpose twice).

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 complexity of coverage products and no output schema, the description thoroughly explains what the tool returns (deductibles, limits, benefits, links, availability), how to get state-correct info, and why it should be used over memory. It covers the essential aspects an agent needs to confidently invoke and interpret the tool.

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 baseline is 3. The description adds meaningful context by explaining the purpose of passing state or location—to get exact SLI limits and online availability restrictions (FL/GA/TX examples). This goes beyond the bare schema descriptions of state and location.

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: explaining Bandago's optional insurance/coverage products. It lists specific products (CDW, DWP, RLP, SLI, PAI, THE WORKS) and distinguishes it from sibling tools like get_rate_quote and check_availability by focusing on coverage details rather than pricing or 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?

Provides explicit usage guidance: 'call this for any question about deductibles or lowering the deductible' and 'Use this instead of quoting coverage details from memory.' Also gives context for when to pass state/location to get state-correct limits and online eligibility, effectively covering when and why to use this tool.

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 fully discloses behavioral traits: it requires both confirmation number and a matching email, returns only customer-facing details, and does not expose internal notes or staff fields. It also implies read-only behavior by directing change/cancel requests elsewhere. No contradictions exist.

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 front-loaded with the primary action, and every subsequent sentence adds value: required fields, returned data, use cases, exclusions, and alternative tools. It is concise yet comprehensive without being verbose.

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 having no output schema, the description lists the specific return fields (status, vehicle, pickup/return dates, locations, totals), which informs the agent about expected output. It also covers authentication requirements, use cases, and when to use other tools. For a self-service lookup of modest complexity, the description is 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?

Schema coverage is 100%, so the baseline is 3. The description adds emphasis ('Requires BOTH...') and clarifies that the email must match the reservation (renter or cardholder), but this information is largely already present in the schema descriptions. No additional syntax or format details are provided beyond the 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?

The description clearly states the tool's purpose: 'Self-service lookup of a customer's own reservation.' It specifies the resource (reservation), the actor (customer), and the typical use cases. It also distinguishes itself from the admin-level lookup_reservation 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?

Explicitly states when to use it: 'Use this to answer what are my dates / where do I pick up / what did I book.' Also gives exclusions and alternatives: 'For changing or cancelling, point the customer to the VIP program' and 'use lookup_reservation (admin) for full staff detail.' This leaves no ambiguity about appropriate usage.

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 full burden of behavioral disclosure. It reveals that the tool uses DIA multipliers, multi-jurisdiction taxes, and clarifies that grand_total includes all optional coverage while subtotal plus sales_tax does not. It also discloses rate_code acceptance rules (display names accepted, brand-only values rejected). This is substantial, though it could mention rate limits or auth if applicable.

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, front-loaded with the core purpose, followed by two concise behavioral clarifications. No wasted words; every sentence earns its place. The structure is efficient and scannable.

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 9-parameter tool with no output schema, the description covers the main output semantics (grand_total vs. subtotal) and the key behavioral nuance of rate_code. It does not enumerate all possible return fields, but that is not expected without an output schema. The description is complete enough for an agent to call it correctly and interpret the primary 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful value beyond the schema by explaining the rate_code acceptance behavior (display names accepted, brand-only values rejected) and the relationship between grand_total, subtotal, and sales_tax, which are not in the schema. It also implies the DIA multiplier effect on pricing, though it doesn't detail every 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?

States a specific verb and resource ('Get a full pricing breakdown for a rental') and enumerates the exact components returned (day rate, insurance, mileage, taxes, grand total). This clearly distinguishes it from siblings like check_availability or list_rate_codes without needing to open them.

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 explains what the tool does and mentions the rate engine and tax logic, but it does not explicitly state when to use this tool versus alternatives. There is no 'use this when you need pricing' or 'for availability use check_availability instead' guidance, so the usage context is implied rather than explicit.

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 of explaining behavior. It usefully clarifies that the customer, not the agent, chooses the vehicle and completes the booking on the website, and that this is the only valid way to produce a booking URL. It does not describe return format, permissions, or other side-effect details, but for a link-generation tool the stated context is reasonably adequate.

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 no wasted words. It front-loads the core purpose, then adds the critical clarification that the customer completes the booking, and finishes with a strong selection rule. 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 rich schema documentation for all 7 parameters, and the description provides the main behavioral context: it generates a pre-filled reservation link, the customer finishes booking on the website, and this is the only valid way to produce such a URL. Since there is no output schema, a slightly more explicit statement of the return value format would make it fully complete, but the word 'link' and the stated purpose largely cover this.

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 parameters are already well documented. The description adds no new parameter-level meaning beyond saying that dates and locations will be pre-filled, which is a minor behavioral note rather than an enrichment of individual parameter semantics. A 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 states a specific action ('Generate a link'), a specific resource ('a reservation on Bandago's website'), and the key behavior of pre-filling dates and locations. It also differentiates itself by noting this is the only valid way to produce a booking URL, which clearly separates it from the 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 'This is the only valid way to produce a booking URL' gives a clear selection rule for when to use this tool. However, it does not explicitly name alternatives or state when NOT to use this tool in favor of siblings like check_availability or list_rate_codes, so it stops just short of a full 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 burden. It discloses that active locations are returned by default and that addresses/contact info are included, which is useful. However, it does not mention whether authentication is required, pagination, or the meaning of 'active', leaving some room for interpretation.

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 concise sentences that front-load the primary purpose and include the key behavioral nuance (default active). No filler or redundant 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?

Given the simple nature of the tool (list locations) and no output schema, the description covers the basic return content (addresses, contact info) and default behavior. It could mention authentication or edge cases, but for a list tool this is reasonably 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 provides 100% coverage for the single parameter 'include_inactive' with a clear description and default. The tool description adds no additional parameter semantics beyond what the schema already states. Baseline 3 is appropriate since schema fully documents the only 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 a specific verb 'List' with a specific resource 'Bandago rental locations', and specifies the returned data (addresses and contact info). It distinguishes from sibling tools like 'get_rate_quote' or 'check_availability' which focus on pricing or 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 implies usage for listing locations and notes default behavior (active only), but does not explicitly state when to use this tool versus alternatives or any exclusions. It provides enough context for a simple list tool, but lacks explicit 'when to use' 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 of behavioral disclosure. It explains the meaning of the returned fields (rate_code vs display_name) and their relationship to other tools, which is helpful. However, it does not explicitly state that the operation is read-only, nor does it mention any permissions, limits, or potential side effects. For a simple listing tool, 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?

The description is two sentences, front-loaded with the core action ('List vehicle types with specs') followed by the crucial detail about the rate_code field. Every sentence adds essential value, and the examples make it concrete without padding. This is a model of concise, well-structured tool documentation.

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, the description covers the key aspects: what the tool returns and how the returned values are used downstream. It explains the distinction between rate_code and display_name, which is critical. However, it does not enumerate all fields returned (e.g., what 'specs' includes), though the examples provide enough context for an agent to proceed. The description is largely 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?

The tool has zero parameters, so the schema coverage is effectively 100%. The description does not need to explain parameters since there are none. It does, however, clarify the meaning of the output fields, which is valuable for the agent. With no parameters, the baseline score of 4 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 verb 'List' and the resource 'vehicle types with specs', making the purpose unambiguous. It further distinguishes this tool from siblings by explicitly noting that the rate_code returned is the exact string consumed by availability, quote, and booking-link tools, which positions it as the canonical source for those values.

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 context: use this tool to obtain the exact rate_code string needed by other tools. It does not explicitly mention when not to use it or name alternatives, but the guidance is specific enough to guide an agent. The emphasis on rate_code being the exact input for other tools implies when this tool is relevant.

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