snaptoll

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

Annotations provide readOnlyHint true, and the description does not contradict. It adds behavioral context: 'CURRENT verified details' implies dynamic data, and notes that toll prices change frequently, which is a key behavioral trait. However, other behaviors like rate limits or error conditions are not mentioned.

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 dense paragraph that front-loads key outputs and usage. Every sentence adds value, but it could be slightly more structured (e.g., bullet points) for easier scanning. Still, it's concise and efficient.

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, the description lists what is returned (price, penalty, etc.) and how to get specific pay-by dates. It covers return values and usage context. However, it does not mention error handling or invalid charge inputs, leaving a minor gap for a query 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 coverage is 100% with descriptions. The description adds value by explaining that passing cross_at yields a concrete pay-by date, and that charge accepts IDs from list_charges or common names—enhancing understanding 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 identifies the tool's purpose: providing verified details for UK toll/road charges, including price, penalty, deadline, and payment instructions. It distinguishes from siblings like list_charges (lists IDs) and check_vehicle (vehicle-specific checks) through specific output details.

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 explicitly states when to use the tool: 'whenever someone asks what a charge costs, says they forgot or missed a payment, asks about the fine/penalty, or asks how or by when to pay'. It also advises to always call this tool rather than relying on memory, providing clear context and alternatives.

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 declare readOnlyHint=true, and the description states 'Free DVLA-backed check', confirming it is a safe read. The description adds behavioral context by listing the specific checks performed (ULEZ, CAZ, LEZ, MOT, tax, make/colour), which goes beyond 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 functionality and followed by usage guidance. No wasted words; every sentence adds value.

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 a single parameter and no output schema, the description provides a comprehensive list of what the tool returns (ULEZ, CAZ, LEZ, MOT, tax, make/colour). This fully informs the agent of the tool's capabilities.

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 the schema describes the single parameter vrn with an example and note about spaces. The description mentions 'from its number plate' but adds no additional meaning or format detail 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 performs a 'Free DVLA-backed check of a UK vehicle from its number plate' and lists specific outputs (ULEZ, CAZ, LEZ, MOT, tax, make/colour). It distinguishes from siblings (charge_info, list_charges) which are about charging, not vehicle checks.

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 explicitly says 'Use when someone asks whether a car is ULEZ/CAZ compliant, or about its MOT or tax.' This provides clear guidance on when to use. It does not mention when not to use, but the use cases are well-defined and the sibling tools cover different domains.

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 indicate readOnlyHint=true; the description adds value by detailing the exact content listed (price, penalty, schemes) without 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 efficiently convey purpose, examples, and usage; no 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 zero parameters and no output schema, the description fully explains what the tool returns, its use case, and how it relates to sibling tools.

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?

No parameters exist, so baseline is 4. The description adds no parameter info, which is appropriate as none are needed.

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 lists UK pay-later ANPR tolls and road charges with specific examples, and explicitly distinguishes its purpose (discovering schemes and IDs) from sibling tools like charge_info.

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 tells when to use it ('Use to discover which schemes exist and the ids accepted by charge_info'), implying the alternative charge_info for details, but does not explicitly state when not to use it.

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