vehicle-data

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 mentions checking 'supported police/Vincario stolen databases,' which implies external data sources and potential rate limits or authentication needs, but doesn't specify these. It also doesn't describe the output format, error handling, or whether the tool is read-only or has side effects. For a tool with no annotations, this leaves significant gaps in understanding its 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. There's no wasted verbiage, and it effectively communicates the essential information in a compact form.

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 moderate complexity (one parameter, no annotations, but with an output schema), the description is minimally adequate. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and parameter specifics. The presence of an output schema means the description doesn't need to explain return values, but it should still address other aspects like when to use it and how it behaves. Overall, it's incomplete but not entirely inadequate.

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 has one parameter ('vin') with 0% description coverage, so the schema provides no semantic details. The description adds context by specifying that the VIN is checked against stolen databases, which clarifies the parameter's purpose. However, it doesn't provide format requirements, examples, or constraints beyond this, leaving some ambiguity. With low schema coverage, the description compensates partially but not fully.

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: 'Check if a VIN appears in supported police/Vincario stolen databases.' It specifies the action ('Check'), resource ('VIN'), and scope ('supported police/Vincario stolen databases'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'vin_decode' or 'vin_decode_info', which might also involve VIN queries but for different purposes.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'vehicle_market_value' or 'vin_decode', nor does it specify prerequisites, exclusions, or contexts for usage. The agent must infer usage based on the purpose alone, which is insufficient for optimal tool selection.

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 states the tool accepts parameters but doesn't describe what the tool does beyond 'Vehicle Market Value for a VIN'—missing details like whether it's a read-only query, requires authentication, has rate limits, or what the output entails. This is inadequate for a tool with no annotation coverage.

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 concise and front-loaded, stating the purpose in the first sentence. The second sentence adds parameter details efficiently. However, it could be slightly more structured by separating purpose from usage instructions, but overall it avoids unnecessary verbosity.

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 has an output schema (which reduces the need to describe return values), but with no annotations and 0% schema description coverage, the description is incomplete. It covers the basic purpose and parameter passing method but lacks behavioral context and detailed parameter semantics, making it only minimally adequate for the 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?

The description adds minimal semantics beyond the input schema: it names 'odometer' and 'odometer_unit' as query parameters and specifies they should be passed via a 'params' dictionary. However, with 0% schema description coverage, it doesn't fully compensate by explaining parameter meanings, formats, or constraints (e.g., what 'odometer_unit' values are allowed). Baseline is 3 due to some added value but incomplete 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 clearly states the tool's purpose: 'Vehicle Market Value for a VIN' specifies both the verb ('Market Value') and resource ('Vehicle'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'vin_decode' or 'vin_decode_info', which might also provide vehicle information, so it misses full sibling distinction.

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 no guidance on when to use this tool versus alternatives. It mentions query parameters but doesn't explain context, prerequisites, or exclusions. Given sibling tools like 'stolen_check' and 'vin_decode', there's no indication of when this tool is preferred, leaving usage unclear.

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 states the tool decodes a VIN and returns detailed information, but doesn't cover aspects like rate limits, authentication needs, error handling, or what 'detailed information' entails. For a tool with no annotations, this is a significant gap in transparency.

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, efficient sentence that directly states the tool's function. It's front-loaded with the core purpose and has no unnecessary words, making it highly concise and well-structured.

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 low complexity (1 parameter) and the presence of an output schema, the description is somewhat complete but lacks depth. It doesn't explain behavioral traits or usage context, which are important since no annotations are provided. The output schema may cover return values, but the description should do more to guide the agent.

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 has 1 parameter with 0% description coverage, so the schema provides no semantic details. The description adds minimal value by mentioning 'VIN' but doesn't explain format, validation, or examples. Since there's only one parameter, the baseline is higher, but the description doesn't fully compensate for the lack of schema details.

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: 'Decode a VIN and return detailed information about the vehicle.' It specifies the action (decode), the resource (VIN), and the outcome (detailed vehicle information). However, it doesn't differentiate from sibling tools like 'vin_decode_info', which appears similar, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'stolen_check' or 'vehicle_market_value', nor does it specify prerequisites or exclusions. Usage is implied by the purpose but not explicitly stated.

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 mentions 'free endpoint', which hints at no cost or authentication needs, but doesn't cover other critical traits like rate limits, response format, error handling, or whether it's read-only (implied by 'list' but not explicit). For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational 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 a single, efficient sentence that front-loads the core purpose ('List which fields are available for decoding a given VIN') and adds a useful note ('free endpoint') without redundancy. Every word earns its place, making it highly concise and well-structured for quick comprehension.

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 low complexity (1 parameter) and the presence of an output schema (which likely details the return values), the description is minimally adequate. However, with no annotations and incomplete parameter semantics, it doesn't fully compensate for missing behavioral context. It covers the basic purpose but leaves gaps in usage and operational details, making it just sufficient for a simple 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?

The input schema has 1 parameter with 0% description coverage, so the schema provides no semantic context. The description adds minimal value by implying the 'vin' parameter is used to query available fields, but doesn't explain format requirements (e.g., VIN length, valid characters) or constraints. With low schema coverage, the description partially compensates but falls short of fully clarifying parameter meaning and usage.

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: 'List which fields are available for decoding a given VIN' specifies the verb ('list'), resource ('fields'), and scope ('for decoding a given VIN'). It distinguishes from sibling tools like 'vin_decode' (which presumably performs the actual decoding) by focusing on metadata about available fields rather than the decoding itself. However, it doesn't explicitly contrast with all siblings (e.g., 'stolen_check', 'vehicle_market_value'), keeping it from a perfect score.

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 mentioning 'free endpoint' and the VIN focus, suggesting this is for informational queries before full decoding. However, it lacks explicit guidance on when to use this tool versus alternatives like 'vin_decode' or other siblings. No when-not-to-use scenarios or prerequisites are stated, leaving the agent to infer optimal usage from the purpose alone.

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