Lie Detector Test

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

Annotations already indicate a read operation (readOnlyHint=true). The description adds valuable behavioral context by listing the returned fields (address, prices, booking link) and confirming search scope (US, Canada, UK). No contradictions.

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, front-loaded with purpose. Every sentence adds value with no redundancy.

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 office locator tool with no output schema, the description sufficiently covers what it does, how to use it, and what it returns. Minor missing details like ordering or pagination are not critical.

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 schema describes both parameters. The description adds usage nuances: examples for query (city, state, country) and default/max for limit, making the parameters more actionable.

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 a specific verb ('Find') and clearly defines the resource ('lie detector (polygraph) test offices near a place'). It distinguishes from siblings, such as 'get_pricing' or 'start_booking', by focusing on discovery rather than transactions.

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 states when to use the tool (search by city, state/region, or country) and specifies supported countries. It does not explicitly mention when not to use or direct users to alternatives, but the context is clear given sibling 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?

Beyond the readOnlyHint annotation, the description discloses that it returns status and location, and crucially highlights that bookings started via start_booking are not confirmed until payment, which is important behavioral context.

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 front-load the main purpose and include an example. Every sentence adds value with no waste.

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 (1 required param, no output schema), the description is complete: it states input format, expected return values, and critical payment 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?

With 100% schema coverage for one parameter, the description adds an example but does not significantly enhance meaning beyond the schema. Baseline 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 checks the status of an existing confirmed booking by its reference, providing a specific example. It distinguishes from sibling tools like start_booking by noting that bookings are not confirmed until payment.

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 after booking creation and clarifies that a booking via start_booking is not confirmed until payment, giving context for when to use. However, it does not explicitly list alternatives or when not to use.

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 provide readOnlyHint and openWorldHint. The description adds details about the return structure (price tiers, deposit) and the deposit's refund policy, which goes beyond annotations and offers useful behavioral context.

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 redundancy. The first sentence states the primary purpose, and the second concisely details the return values and a key behavioral fact (deposit refund policy).

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?

There is no output schema, but the description outlines the return structure (price tiers, deposit) and a policy detail. It does not mention error handling or edge cases, but for a pricing tool this is largely sufficient, leaving little ambiguity.

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% for the single parameter 'query'. The description repeats the schema's explanation (city/state/country with examples) without adding new meaning, so it meets the baseline of 3.

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 starts with 'Get polygraph examination pricing for a location,' which clearly states the verb (get), resource (pricing), and scope (for a location). This distinguishes it from siblings like find_offices and start_booking.

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 is clear about what the tool returns but does not explicitly contrast with alternatives or mention when not to use it. However, the purpose is specific enough that usage context is implied, and no misleading guidance is present.

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?

The description is consistent with the readOnlyHint annotation, indicating a safe read operation. No additional behavioral traits beyond what annotations provide are disclosed. The description adds minimal value beyond the annotation.

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 purpose and lists key topics. It is concise without unnecessary 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 tool's simplicity (no parameters, no output schema), the description thoroughly explains the scope of information returned, covering all relevant aspects of the service. It is complete for the intended 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?

With no parameters and 100% schema coverage, the baseline is 3. The description notes 'Takes no arguments,' which restates the schema but adds no new semantic meaning.

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 explicitly states the tool retrieves general information about the lie detector test service, listing specific topics such as offerings, booking, deposit/refund policy, service area, and post-booking. This clearly distinguishes it from sibling tools like find_offices or get_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 implies use for an overview of the service, but does not explicitly state when to use versus when to use siblings or provide exclusion criteria. The context makes it clear enough, but lacks explicit directives.

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 indicate a non-read-only (mutation) and non-destructive operation. The description adds that the tool returns a reference and a booking link with office pre-filled, and emphasizes that an agent cannot complete the booking, providing behavioral context beyond 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 short paragraphs: the first states the purpose and output, the second provides critical usage instructions. Every sentence adds value without redundancy, and the important warning is front-loaded.

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 no output schema and 7 parameters, the description covers the essential return values (reference and booking link) and the key user completion steps. It lacks details on error handling or booking link expiration, but is generally complete for the tool's complexity.

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% with clear parameter descriptions. The description adds the important note about not putting contact details in URLs, but does not elaborate on parameter meaning beyond the schema, so a baseline score 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 'Start a lie detector test booking for a person' with a specific verb and resource. It clearly distinguishes from sibling tools like find_offices or get_booking_status, as it initiates a new booking process.

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 when to use the tool (to start a booking) and importantly warns that the person must complete the process themselves, including choosing time, signing agreement, and paying. It does not explicitly mention alternatives, but the context is sufficient.

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