Vehicle Database MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion, such as multiple decode variants (decode_by_vin, vin_decode, universal_vin_decode, etc.) and numerous makes/models/trims/years tools with similar names but different suffixes. While descriptions provide some differentiation, the sheer number of similar tools makes it difficult for an agent to reliably choose the correct one without deep inspection of each description.
Naming Consistency2/5
Naming is inconsistent with mixed conventions: some use snake_case (by_plate_and_state), others use underscores with numbers (makes_2, models_3), and some are single words (auction, decode). There's no consistent verb_noun pattern, and the numbered suffixes for similar tools (makes, makes_2, makes_3, etc.) are particularly confusing and non-standard.
Tool Count2/5
With 61 tools, this is far too many for coherent use. The server appears to expose every possible API endpoint as a separate tool rather than consolidating related functionality. While the vehicle database domain is broad, this many tools creates overwhelming complexity and suggests poor API design for agent consumption.
Completeness4/5
Despite the organizational issues, the tool set appears remarkably complete for vehicle data lookup. It covers VIN decoding, license plate lookup, market values, maintenance, recalls, warranties, sales history, stolen checks, title checks, and more across multiple regions. The main gap appears to be vehicle creation/modification tools, but as a lookup-focused server, it provides comprehensive coverage.
Average 2.8/5 across 61 of 61 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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This repository is licensed under MIT License.
This repository includes a README.md file.
Tools from this server were used 6 times in the last 30 days.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
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 'US/CAD region' but gives no details on what the tool does (e.g., read-only vs. mutation, data returned, error handling, rate limits, or authentication needs). For a tool with unknown behavior and no structured hints, this is a critical gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly concise to the point of under-specification—'Support: US/CAD region' is a fragment that fails to convey essential information. While brief, it lacks structure and front-loads no useful details, making it inefficient rather than succinct. Every sentence should earn its place, but this one adds minimal value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by sibling tools (e.g., decoding, checks) and no annotations or output schema, the description is incomplete. It doesn't explain what the tool returns, its behavior, or how it differs from similar tools. For a tool in a crowded namespace with no structured support, this leaves the agent without necessary context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'vin' parameter documented as a string (though the example is empty). The description adds no meaning beyond the schema—it doesn't clarify VIN format, validation, or regional constraints. With high schema coverage, the baseline is 3, as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Support: US/CAD region' is vague and tautological—it restates the name 'by_vin' without specifying what the tool does (e.g., decode VINs, check vehicle data). It mentions a region but lacks a clear verb+resource statement. Compared to siblings like 'decode_by_vin' or 'vin_decode', it fails to distinguish its purpose, leaving the agent guessing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'decode_by_vin', 'cad_decode_by_vin', 'us_decode'), the description offers no context, exclusions, or prerequisites. This forces the agent to infer usage from the name alone, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 coverage years and regions but omits critical details: what data is returned (e.g., vehicle specs), whether it's a read-only operation, error handling, rate limits, or authentication needs. This leaves the agent with insufficient information to invoke the tool safely and effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two brief phrases, but it's under-specified rather than efficiently informative. It wastes space on tautological elements (e.g., 'CAD' in the name) instead of front-loading actionable details, making it less helpful despite its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of VIN decoding and the lack of annotations and output schema, the description is severely incomplete. It fails to explain what the tool returns, error conditions, or behavioral traits, leaving the agent without enough context to use the tool correctly in a real-world scenario.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'vin' documented in the schema. The description adds no parameter-specific information beyond what the schema provides (e.g., format requirements, validation rules). According to the rules, with high schema coverage, the baseline score is 3, as the description doesn't compensate but doesn't need to heavily.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Coverage: 1981- 2026 Support: CAD and US regions' is vague and tautological—it restates the tool name 'cad_decode_by_vin' without specifying what the tool actually does (e.g., decode VINs for Canadian and US vehicles). It fails to distinguish from siblings like 'by_vin', 'decode_by_vin', or 'us_decode', leaving the agent guessing about its unique function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'by_vin', 'us_decode', 'decode_by_vin'), the description offers no context, exclusions, or prerequisites, making it impossible for an agent to choose appropriately among them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. The description only mentions coverage and support regions, failing to disclose any behavioral traits—such as what data is returned, error handling, rate limits, or authentication needs—making it inadequate for a tool with no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence, but it is not front-loaded with purpose—it leads with coverage details rather than the core function. While efficient in length, the structure could be improved by prioritizing clarity over brevity, as it lacks essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by many sibling tools and the lack of annotations and output schema, the description is incomplete. It fails to explain what the tool returns (e.g., decoded vehicle attributes), how it differs from similar tools, or any operational constraints, leaving significant gaps for an agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter 'vin' documented as a string. The description adds no meaning beyond the schema, as it does not explain parameter semantics (e.g., VIN format requirements or validation). However, with high schema coverage and only one parameter, the baseline score of 3 is appropriate, as the schema adequately defines the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Coverage: 1981-Present Support: US/CAD/EU' is vague and tautological—it restates the tool's name 'universal_vin_decode' by implying broad coverage without specifying what the tool actually does (e.g., decode VINs to extract vehicle details). It fails to distinguish this tool from many sibling tools like 'by_vin', 'decode_by_vin', or 'vin_decode', which likely perform similar functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With numerous sibling tools (e.g., 'us_decode', 'eu_decode', 'cad_decode_by_vin'), the description offers no explicit or implied context for selection, such as regional specificity or feature differences, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. However, it only mentions coverage and support regions without describing what the tool does (e.g., whether it performs a lookup, returns data, or modifies something), its output format, error handling, or any constraints like rate limits or authentication needs. This leaves critical behavioral traits unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with only one sentence, making it front-loaded and free of unnecessary words. Every part ('Coverage: 1981-2026 Support: CAD and US regions') directly contributes information, though it is insufficient in content. There is no wasted text, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks essential details such as the tool's function, output expectations, and usage context. While the schema covers parameters well, the description fails to provide a holistic understanding, making it inadequate for effective tool selection and invocation by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with each parameter documented (e.g., 'year', 'make', 'model', 'trim'). The description does not add any meaning beyond the schema, such as explaining parameter relationships or valid values. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema adequately handles parameter documentation without additional description input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Coverage: 1981-2026 Support: CAD and US regions' which provides some context about temporal and regional scope, but does not clearly state what the tool actually does. It lacks a specific verb (e.g., 'decode', 'look up', 'retrieve') and does not mention the resource (e.g., 'vehicle information', 'VIN details'). This makes the purpose vague compared to sibling tools like 'decode_by_ymmt' which suggests decoding functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 does not mention any prerequisites, exclusions, or comparisons to sibling tools (e.g., 'cad_decode_by_vin', 'decode_by_ymmt', 'us_decode'), leaving the agent with no information to make an informed choice among similar decoding tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool includes vehicle specifications and images, which adds some behavioral context about the return content. However, it doesn't disclose critical traits like whether it's a read-only operation, requires authentication, has rate limits, or what happens if no data is found. For a tool with zero annotation coverage, 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and relatively concise, but it could be more front-loaded with the core action. It starts with 'Auction API comprises of...' which is a bit wordy and could be streamlined. While not overly verbose, it doesn't maximize clarity efficiently, and some phrases like 'comprises of' are redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that likely returns complex data (specifications and images). It mentions the data includes specifications and images, which is helpful, but doesn't cover return format, error handling, or prerequisites. With low annotation richness and schema-only parameter info, it should provide more context to be fully adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage ('vin' with an example), so the schema does the heavy lifting. The description doesn't add any parameter-specific information beyond implying it might use VIN for lookup, but it doesn't explain parameter usage or constraints. Baseline is 3 since schema coverage is high, and no additional param semantics are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool provides 'information of instance of a vehicle being put up for sale through a vehicle auction in North America,' which indicates it retrieves auction data. However, it's vague about the specific action (e.g., 'get' or 'search') and doesn't clearly differentiate from siblings like 'sales_history' or 'market_value_by_vin,' which might also relate to vehicle sales data. It restates the tool name 'auction' in the context but lacks a precise verb-resource combination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. Sibling tools include many decode, market value, and check tools (e.g., 'by_vin', 'sales_history'), but the description doesn't specify if this is for current auctions, historical data, or how it differs. It mentions North America scope, but no explicit when/when-not or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the API's coverage (1999-2023 manufacturers) and general data types (battery capacity, range, etc.), but doesn't disclose critical behavioral traits: what happens if parameters don't match existing vehicles (error behavior), whether this is a read-only operation, authentication requirements, rate limits, or what the output format looks like. The description is too generic about the API rather than specific to this tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise (two sentences) but poorly structured. The first sentence is overly broad about the API rather than this specific tool. The second sentence repeats coverage information. It's not front-loaded with the tool's specific purpose, and some content (like 'Apart from basic specifications...') doesn't directly help the agent understand how to use this tool effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 required parameters, no output schema, no annotations, and many similar sibling tools), the description is incomplete. It doesn't explain what this tool returns, how it differs from other decode tools, what happens when parameters don't match, or provide enough context for the agent to use it correctly among alternatives. The description focuses on API capabilities rather than tool-specific implementation details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters documented in the schema (year, make, model, trim). The description adds no parameter-specific information beyond what's already in the schema - it doesn't explain how these parameters work together to identify a vehicle, what format they should be in, or provide examples beyond the schema's basic examples. With high schema coverage, the baseline is 3 even without additional param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a general overview of what the API does ('gives detailed specifications on any electric car') but doesn't specify what the 'decode' tool itself does. It doesn't clearly state the verb+resource combination (e.g., 'retrieve specifications for a specific EV configuration') and doesn't distinguish this tool from its many siblings (like decode_by_vin, decode_by_ymmt, etc.). The description is more about the API's capabilities than this specific tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions coverage (manufacturers from 1999-2023) but provides no guidance on when to use this tool versus alternatives like decode_by_vin, decode_by_ymmt, or other decode variants. There's no explicit when/when-not usage advice or comparison to sibling tools, leaving the agent to guess which tool is appropriate for which scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes what information the decode returns (interior/exterior details, HEX codes, fuel economy, etc.) but doesn't mention important behavioral aspects like rate limits, authentication requirements, error conditions, or whether this is a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise but poorly structured. It starts with 'Premium plus VIN decode' which doesn't match the tool name 'multiple_trims', creating confusion. The information is front-loaded with feature details but lacks clear organization. The description cuts off mid-sentence ('S...'), indicating it's incomplete.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is incomplete. It describes what information the service provides but doesn't explain the tool's specific function, how it differs from siblings, or what the output format looks like. Given the complexity implied by the tool name and the rich sibling ecosystem, this description leaves too many questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description doesn't add any meaningful information about the 'vin' or 'multiple' parameters beyond what the schema provides. It doesn't explain what 'multiple' means in the context of this tool or how it affects the response. Baseline 3 is appropriate when schema does the documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explains what 'Premium plus VIN decode' does but doesn't clearly state what the 'multiple_trims' tool specifically does. It describes a general VIN decoding service rather than explaining how this tool differs from siblings like 'by_vin', 'decode_by_vin', or 'vin_decode'. The tool name suggests something about multiple trims, but the description doesn't connect to this purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus the many other VIN-related tools in the sibling list. The description mentions coverage for 1999-2023 VINs, but doesn't specify when this 'premium plus' decode should be chosen over basic decode tools or what makes it different from similar-sounding tools like 'vin_decode' or 'decode_by_vin'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It implies a read-only operation by stating it 'provides information,' but doesn't disclose behavioral traits like whether it requires authentication, rate limits, error conditions, or what format the maintenance schedule information returns. The VIN coverage statement adds some context but is inconsistent with the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is poorly structured with run-on sentences and lacks clear organization. It front-loads the purpose but includes contradictory information about VIN that doesn't match the schema. The sentences about coverage and support don't earn their place as they mislead rather than clarify.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It fails to explain what the output looks like (e.g., maintenance schedule details), doesn't clarify the parameter mismatch, and offers minimal behavioral context. For a tool with 3 required parameters and no structured output documentation, this leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter having example values in the schema. The description doesn't add any meaningful parameter semantics beyond what's in the schema—it mentions VIN requirements that don't align with the actual parameters. Baseline is 3 since the schema does the heavy lifting, but the description adds confusion rather than value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool provides 'information about the OEM vehicle maintenance schedules at mileage intervals' which clarifies it's a read operation about maintenance schedules. However, it doesn't specify what kind of information (e.g., schedule details, intervals, recommended services) or distinguish itself from sibling tools like 'vehicle_repair' or 'vehicle_warranty' that might also provide vehicle-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions coverage for VINs from 2001-2022 and requires a 17-digit VIN, but this contradicts the input schema which requires year, make, and model parameters (not VIN). There's no guidance on when to use this tool versus alternatives like 'decode_by_vin' or 'vehicle_repair', and the VIN mention is confusing given the actual parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the API returns 'a wide range of data points' and 'comprehensive and accurate' information with coverage from 1980-present, which gives some context on scope and reliability. However, it lacks details on rate limits, authentication needs, error handling, or response format, which are critical for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but front-loaded with marketing language ('API is built to return...', 'designed for customers and businesses...') before stating the core function. It could be more direct, and the mention of 'VIN descriptions' is potentially misleading given the tool's name and parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a tool with 4 parameters for vehicle decoding among many siblings, the description is incomplete. It fails to clarify the exact use case, differentiate from alternatives, or provide sufficient behavioral context (e.g., what 'comprehensive' data includes), leaving gaps for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear examples for each parameter (year, make, model, trim). The description adds no specific parameter semantics beyond implying these inputs are used to identify a vehicle, which is already obvious from the parameter names. Baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description vaguely states it returns 'vehicle specifications, features, and equipment' for a 'complete picture of any vehicle', but it doesn't specify the exact action (e.g., 'decode' or 'look up') or clearly differentiate from many sibling tools like 'decode_by_vin' or 'ymm'. It mentions 'VIN descriptions' which is confusing since the tool uses YMMT parameters, not VIN.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'decode_by_vin' or 'ymm' is provided. The description mentions 'customers and businesses in need of complete vehicle specifications', but this is generic and doesn't help an agent choose between the many decoding tools available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'API provides' but doesn't disclose behavioral traits like rate limits, authentication needs, error conditions, or response format. The description is minimal and lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core purpose, though it could be more structured (e.g., separating purpose from context).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a tool with 2 required parameters, the description is incomplete. It doesn't explain what 'oil type data and specifications' includes, how results are returned, or any limitations, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('plate', 'state') documented in the schema. The description adds no parameter-specific semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'provides oil type data and specifications by plate and state', which gives a general purpose (verb: 'provides', resource: 'oil type data and specifications') but is vague about scope and format. It doesn't distinguish from sibling tools like 'us_plate_decode' or 'license_plate_ocr' that might also use plate/state inputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, constraints, or comparison to sibling tools (e.g., 'by_vin' for VIN-based lookups vs. plate-based). Usage is implied by the name but not explained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While it states what the API provides (oil type and specifications), it doesn't disclose behavioral aspects like authentication requirements, rate limits, error conditions, response format, or whether this is a read-only operation. The description is purely functional without operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core functionality. There's no wasted verbiage or unnecessary elaboration. However, it could be more front-loaded with distinguishing information given the many similar tools in the sibling list.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'specifications' includes, the format of returned data, error handling, or prerequisites. Given the complexity implied by returning technical specifications and the lack of structured metadata, more descriptive context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single 'vin' parameter documented in the schema. The description doesn't add any parameter semantics beyond what the schema provides (it mentions 'by vin' but doesn't explain VIN format requirements, validation, or examples). With complete schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool retrieves 'oil type and its specifications by vin', which is a clear verb+resource combination. However, it doesn't distinguish this tool from similar sibling tools like 'by_vin', 'decode_by_vin', or 'vin_decode', leaving ambiguity about how this specific oil-focused lookup differs from general VIN decoding tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. With multiple VIN-related tools available (by_vin, decode_by_vin, vin_decode, etc.), the description doesn't indicate whether this is for maintenance purposes, what makes it different, or when another tool might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 providing 'information' but doesn't specify if this is a read-only operation, what data formats or structures are returned, potential errors (e.g., invalid VIN handling), or any rate limits or authentication needs. For a tool with no annotation coverage, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words, making it easy to parse quickly. However, it could be more structured by explicitly separating analytics from basic info, but overall, it's appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of vehicle data tools, no annotations, no output schema, and many similar siblings, the description is incomplete. It doesn't explain return values, error conditions, or how it differs from other decode tools. For a tool in a crowded namespace, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage, providing an example value. The description adds no additional meaning beyond implying the VIN is used to fetch analytics and vehicle info. Since schema coverage is high, the baseline is 3, as the description doesn't compensate with extra details like VIN format requirements or regional constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Provides the information of VIN analytics and basic vehicle information including specifications,' which clarifies it returns data for a given VIN. However, it's vague about the exact scope (e.g., what 'analytics' entails) and doesn't distinguish it from many sibling tools like 'by_vin', 'decode_by_vin', or 'vin_decode', which likely have similar purposes. This lack of differentiation reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With numerous sibling tools related to VIN decoding (e.g., 'by_vin', 'decode_by_vin', 'vin_decode'), there's no indication of unique features, regional focus (implied by 'eu' in the name but not stated), or specific use cases. This absence leaves agents guessing about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it 'provides detailed information', implying a read-only operation without specifying data format, rate limits, authentication needs, or error handling. It lacks behavioral details like whether it returns structured data, pagination, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that directly state the tool's function and coverage range. It's front-loaded with the main purpose, though the second sentence could be integrated more smoothly for better flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool with 3 parameters. It doesn't explain return values, error cases, or how the output relates to parts information, leaving significant gaps in understanding the tool's behavior and results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema documents the three required parameters (year, make, model). The description adds no parameter-specific information beyond implying they relate to vehicle identification, maintaining the baseline score of 3 as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'provides detailed information about vehicle parts with price and labor cost', which gives a general purpose but lacks specificity about what kind of information or format. It doesn't clearly distinguish from sibling tools like 'vehicle_maintenance' or 'vehicle_recall', making it somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions coverage for manufacturers from 1992 to 2022, which provides some context, but offers no explicit guidance on when to use this tool versus alternatives like 'decode', 'by_vin', or other vehicle-related tools. There are no usage exclusions or comparisons to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the data source (millions of sales, frequently updated) and output types (trade-in, private party, retail values), but lacks critical behavioral details such as rate limits, authentication requirements, error handling, or response format. This is a significant gap for a tool with no structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose and repetitive, with multiple sentences reiterating the same points about market value analysis and data sources. It could be more front-loaded and concise, as phrases like 'We analyze vehicle market value data...' and 'Our database is updated frequently...' add little unique value and detract from clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It lacks details on return values (e.g., structure of market value estimates), error cases, or operational constraints like rate limits. For a tool with one parameter but significant behavioral complexity, this leaves the agent under-informed about how to effectively use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the 'vin' parameter documented in the schema. The description adds context by specifying it's for vehicle market values and mentions North America, but doesn't provide additional semantics beyond what the schema already covers (e.g., VIN format requirements or examples beyond the schema's example). Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves market values (trade-in, private party, retail) for vehicles by VIN, with specific mention of the resource (vehicle market value) and scope (North America, based on sales data). However, it doesn't explicitly distinguish this from sibling tools like 'market_value_by_ymm' or 'makes_market_value', which likely serve similar purposes with different inputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description mentions it's for market values by VIN, but doesn't compare it to siblings like 'by_vin' or 'market_value_by_ymm', leaving the agent to infer usage based on tool names alone without explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool provides 'instant access' and 'accurate' estimates based on market analysis, but doesn't specify rate limits, authentication requirements, data freshness ('upda...' is cut off), error conditions, or response format. Key operational details are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose and repetitive, with multiple sentences about the service's capabilities and data sources that don't directly help tool selection. It's cut off mid-sentence ('upda...'), suggesting incomplete editing. The core purpose is buried in promotional language rather than front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 required parameters, no annotations, and no output schema, the description is inadequate. It lacks crucial information about response format, error handling, authentication, rate limits, and differentiation from sibling tools. The incomplete sentence further reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear parameter descriptions in the schema. The description adds context that these parameters represent 'year, make, model and trim' for vehicle identification, but doesn't provide additional semantic details beyond what's already in the schema (e.g., format requirements, trim handling). Baseline 3 is appropriate given complete schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get access to trade-in, private party and retail values for any vehicle' and specifies it provides market value estimates based on year, make, and model. It distinguishes itself from siblings like 'market_value_by_vin' by using YMM parameters instead of VIN, though this distinction could be more explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the tool provides market values for vehicles by year, make, and model, but offers no guidance on when to use this versus alternatives like 'market_value_by_vin' or 'by_plate_and_state'. There's no mention of prerequisites, limitations, or comparative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Get the model list' which implies a read-only operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, error handling, or what the output looks like (e.g., list format, pagination). For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste: 'Get the model list by year and make'. It's front-loaded and appropriately sized for its purpose, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain the return values (e.g., what a 'model list' contains), error cases, or how it differs from similar tools. For a tool in a crowded namespace with many siblings, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (year and make) documented in the schema. The description adds minimal value by mentioning these parameters ('by year and make'), but doesn't provide additional meaning like expected formats (e.g., 'year' as YYYY), examples, or constraints beyond what's in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the model list by year and make' clearly states the action (get) and resource (model list), but it's vague about what 'model list' refers to (vehicle models? data models?) and doesn't distinguish this tool from sibling tools like 'model', 'models_2' through 'models_7', or 'models_market_value'. It provides basic purpose but lacks specificity and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. With many sibling tools like 'model', 'decode', and 'makes', there's no indication of context, prerequisites, or exclusions. Usage is implied only by the parameters (year and make), but no explicit guidelines are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states it's a listing operation, implying read-only behavior, but doesn't disclose any behavioral traits such as pagination, rate limits, authentication needs, or what happens if no year is provided. The description is too minimal to provide adequate transparency 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's appropriately sized for a simple tool, though it could be more front-loaded with additional context to improve clarity without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what 'makes' are, how results are returned, or any error conditions. For a tool with minimal structured data, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by specifying that listing is 'by year', which aligns with the single parameter 'year' in the schema. Schema description coverage is 100%, so the schema already documents the parameter well. With 0 required parameters, the baseline is high, and the description complements this by clarifying the tool's purpose in relation to the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List down the makes by year' states a clear verb ('List down') and resource ('makes'), but it's vague about scope and doesn't distinguish from sibling tools like 'makes', 'makes_2', etc. It specifies 'by year' which adds some context, but doesn't clarify what 'makes' refers to (e.g., vehicle makes) or how this differs from similar tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'makes', 'makes_2', or other sibling tools. The description implies it's for listing makes filtered by year, but doesn't specify prerequisites, exclusions, or when to choose this over other makes-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions global coverage and VIN format support but fails to describe critical traits like authentication requirements, rate limits, error handling, or what the output looks like (e.g., boolean result, detailed records). For a tool that queries sensitive data, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured into three sentences but includes redundant phrasing (e.g., repeating 'API' and global aspects). Sentences like 'This API supports a wide range of VIN formats, ensuring compatibility across multiple regions and vehicle types' could be more concise. It's front-loaded with the core purpose but has some fluff that doesn't add critical value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a stolen vehicle check (sensitive data query) and the absence of annotations and output schema, the description is incomplete. It lacks details on behavioral traits (e.g., privacy implications, response format), usage context compared to siblings, and output specifics. This makes it inadequate for an agent to use the tool effectively without guesswork.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'vin' parameter documented as a string with an example. The description adds context by specifying supported VIN formats (17-characters and classic 5-13 digits) and global compatibility, which provides useful semantic information beyond the schema. However, it doesn't detail validation rules or error cases, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 for any history of theft associated with a vehicle by VIN, providing stolen record information.' It specifies the verb ('check'), resource ('stolen record information'), and key input ('VIN'). However, it doesn't explicitly differentiate from sibling tools like 'title_check' or 'by_vin', which might have overlapping functionality, preventing 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.
Usage Guidelines2/5
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 VIN formats and global coverage but doesn't compare to siblings like 'by_vin', 'by_vin_2', or 'title_check', leaving the agent without context for selection. This lack of comparative information results in a low score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the tool is an 'API' and requires a 'valid UK registration number', hinting at external dependencies and input validation. However, it lacks details on error handling, rate limits, authentication needs, or what '& more' includes in the output. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words, though '& more' is vague. Every part contributes to understanding the tool's function, making it appropriately concise for a simple lookup tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 2 parameters with full schema coverage, the description is minimally adequate. It covers the basic purpose and input requirement but lacks details on output structure, error cases, or integration context. For a decode tool with external API dependencies, more completeness would be helpful, but it meets the minimum viable threshold.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters 'reg' and 'for_api_portal' are documented in the schema. The description adds no specific parameter semantics beyond implying 'reg' is a UK registration number. It doesn't explain the purpose of 'for_api_portal' or provide format examples. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 API requires a valid UK registration number to return the year, make, model, color, engine details & more.' It specifies the verb ('decode'), resource ('UK registration number'), and output details. However, it doesn't explicitly differentiate from sibling tools like 'decode' or 'us_plate_decode', which likely handle different regions or formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance: it implies use when you have a UK registration number. It doesn't specify when to choose this tool over alternatives (e.g., 'decode' for general use, 'us_plate_decode' for US plates), nor does it mention prerequisites like API access or valid registration formats. No explicit when-not or alternative recommendations are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It states what the tool returns but doesn't mention authentication needs, rate limits, error conditions, image format requirements, or processing time. The description doesn't contradict annotations (none exist), but provides inadequate behavioral disclosure for an OCR tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two clear sentences. It's front-loaded with the core functionality, though it could be slightly more structured by separating USA/UK functionality more clearly. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an OCR tool with no annotations and no output schema, the description is insufficient. It doesn't explain return format, error handling, image requirements, or how to interpret results. Given the complexity of image processing and lack of structured output documentation, more completeness is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 adds no parameter-specific information beyond what the schema provides (file and url parameters). It doesn't clarify whether both parameters are mutually exclusive, preferred formats, or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it returns license plate information (number and state for USA, number for UK) from an image. It specifies the verb 'returns' and resource 'license plate number/state', but doesn't explicitly differentiate from sibling tools like 'us_plate_decode' or 'vin_ocr'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 USA vs UK differences but doesn't explain when to choose this over other plate/vehicle-related tools in the sibling list, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'Provides a list', implying a read-only operation, but does not cover other behavioral aspects such as error handling, rate limits, authentication needs, or what the output looks like (since there is no output schema). This leaves significant gaps for a tool with two required parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
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 is appropriately sized and front-loaded, making it easy to understand quickly. However, it could be slightly improved by including key usage details to enhance clarity without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of having two required parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines, which are crucial for the agent to operate this tool effectively. The description does not compensate for the missing structured data, leaving significant contextual gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning the input schema already documents both parameters ('year' and 'data') with descriptions. The description adds minimal value by implying the 'year' parameter is used to filter makes, but it does not explain the 'data' parameter's role or provide additional context beyond the schema. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of makes available for vehicle maintenance API by year.' It specifies the verb ('Provides a list'), resource ('makes'), and context ('vehicle maintenance API'), which is clear and specific. However, it does not explicitly distinguish this tool from its many sibling tools (e.g., 'makes_2', 'makes_3', etc.), which would be needed for a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'by year' but does not specify prerequisites, exclusions, or compare it to sibling tools like 'makes_2' or 'makes_3'. Without any usage context, the agent must infer when this tool is appropriate, leading to potential confusion given the many similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 retrieves data ('Get'), implying a read-only operation, but doesn't specify aspects like rate limits, authentication needs, error handling, or the format of the returned list. For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words, clearly front-loading the purpose. It efficiently communicates the core functionality without unnecessary elaboration, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns a list of makes. It doesn't explain the return format, potential errors, or how to handle the data, leaving gaps in understanding the tool's full behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'by year', which aligns with the single parameter 'year' in the input schema. Since schema description coverage is 100%, the schema already documents the parameter adequately, so the description adds minimal value beyond restating the parameter's role. Baseline 3 is appropriate as the schema handles most of the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the makes list by year' clearly states the action (Get) and resource (makes list), with the parameter 'year' specifying the scope. However, it doesn't differentiate from sibling tools like 'makes_2' through 'makes_7' or 'make', which likely serve similar purposes, leaving some ambiguity about when to choose this specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'makes_2' to 'makes_7', 'make'), the description lacks context on prerequisites, exclusions, or comparisons, leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'provides a list' but doesn't describe output format, pagination, rate limits, authentication needs, or error handling. For a tool with no annotation coverage, this is a significant gap, as the agent lacks essential operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It's appropriately sized for a simple tool, with zero wasted words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list format, data fields), potential errors, or how it integrates with the 'Advanced Decode API'. For a tool in a complex domain with many siblings, more context is needed to ensure the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter 'year' documented. The description adds minimal value beyond the schema by implying the parameter is used for filtering ('by year'), but it doesn't provide additional semantics like format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of makes available for Advanced Decode API by year.' It specifies the verb ('provides'), resource ('list of makes'), and scope ('by year'), which is clear. However, it doesn't explicitly distinguish this tool from sibling tools like 'makes', 'makes_3', etc., which appear to serve similar functions, so it doesn't fully differentiate from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 the 'Advanced Decode API' but doesn't specify context or prerequisites, and with multiple sibling tools (e.g., 'makes', 'makes_3'), there's no indication of differences or when this specific tool is preferred. This lack of guidance leaves the agent uncertain about selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 the tool 'provides a list' but doesn't specify output format (e.g., JSON array, pagination), error handling, rate limits, or authentication requirements. For a tool with no annotations, this leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently states the tool's purpose without unnecessary details. It is front-loaded and easy to parse, though it could be slightly more structured (e.g., by explicitly mentioning parameters). Overall, it earns its place with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of sibling tools and lack of annotations or output schema, the description is incomplete. It doesn't clarify how 'makes_3' differs from other 'makes' tools, what the output looks like, or any behavioral traits. For a tool in a crowded namespace with no structured support, more context is needed to guide effective usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('year' and 'data') documented in the schema. The description adds minimal value beyond the schema by implying the tool is for 'vehicle warranty API', but it doesn't explain parameter meanings (e.g., why 'data' is required and set to 'warranty', or the format for 'year'). Baseline 3 is appropriate since the schema handles most of the documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of makes available for vehicle warranty API by year.' It specifies the verb ('provides'), resource ('list of makes'), and context ('vehicle warranty API by year'). However, it doesn't explicitly differentiate from sibling tools like 'makes', 'makes_2', 'makes_4', etc., which appear to serve similar functions, leaving some ambiguity about when to choose this specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With multiple sibling tools (e.g., 'makes', 'makes_2', 'makes_4'), there is no indication of what distinguishes 'makes_3' from others, such as different data sources, scopes, or API versions. This lack of context makes it difficult for an agent to select the correct tool without trial and error.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 it 'provides a list,' implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or output format. For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core functionality without unnecessary words. It's front-loaded with the main action and resource, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (one parameter, no output schema, no annotations), the description is minimal. It states what the tool does but lacks details on usage guidelines, behavioral traits, or how it fits among siblings. For a tool in a crowded namespace with no output schema, more context would be helpful to ensure proper agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter 'year' documented as 'Example value: '. The description adds context by specifying it's for 'YMM specifications API by year,' which ties the parameter to the tool's purpose. However, it doesn't provide additional details like format or constraints beyond what the schema implies, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of makes available for YMM specifications API by year.' It specifies the verb ('provides'), resource ('list of makes'), and context ('YMM specifications API by year'). However, it doesn't explicitly differentiate from sibling tools like 'make', 'makes', 'makes_2', etc., which appear to be related, so it doesn't reach the highest clarity level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With many sibling tools (e.g., 'make', 'makes', 'makes_2' through 'makes_7', 'makes_market_value'), there's no indication of how this tool differs or when it's appropriate. The context 'by year' is implied but not compared to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes a read operation ('provides a list'), which implies it's non-destructive, but doesn't mention any behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
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 without unnecessary words. It's front-loaded with the core purpose, making it easy to parse. However, it could be slightly more structured by explicitly mentioning the parameter, but overall it's concise and well-formed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter with full schema coverage and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, and differentiation from siblings. For a simple read tool, it meets the bare minimum, but improvements in guidelines and transparency would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantic context by implying the 'year' parameter is used to filter makes for vehicle recalls, but the input schema already has 100% coverage with a clear description for the 'year' parameter. Since schema coverage is high, the baseline is 3, and the description doesn't provide additional details like format examples or constraints beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of makes available for vehicle recalls API by year.' It specifies the verb ('provides a list'), resource ('makes'), and context ('for vehicle recalls API by year'). However, it doesn't explicitly differentiate from sibling tools like 'makes', 'makes_2', 'makes_3', etc., which appear to be similar list operations, leaving some ambiguity about why this specific tool exists among them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'by year' as a parameter, but doesn't specify prerequisites, exclusions, or compare it to other 'makes' tools in the sibling list. This lack of context makes it unclear why an agent would choose this over other similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions it 'provides a list' but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or error handling. For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose without unnecessary words. Every part earns its place by specifying the action, resource, and context concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list format, fields), error conditions, or prerequisites. For a tool in a complex server with many siblings, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the 'year' parameter fully documented in the schema. The description adds no additional parameter semantics beyond implying the year is used to filter makes for market value, which is already clear from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('provides') and resource ('list of makes'), specifying it's for the 'market value API by year'. It distinguishes from generic 'makes' tools by mentioning the market value context, though it doesn't explicitly differentiate from similar tools like 'makes' or 'makes_2'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'makes', 'makes_2', 'market_value_by_vin'), the description lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes the tool as providing a list, which implies a read-only operation, but doesn't address key behavioral aspects such as error handling, rate limits, authentication needs, or what happens if parameters are invalid. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, straightforward sentence that efficiently conveys the core purpose without unnecessary details. It's front-loaded with the main action and context, making it easy to parse. However, it could be slightly more structured by explicitly listing parameters or usage scenarios, but it avoids waste and is appropriately concise for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool with 3 required parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., format of the list), error conditions, or how it integrates with the 'vehicle maintenance API' context. For a tool in a server with many siblings, more contextual detail is needed to ensure the agent can use it effectively without confusion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with each parameter ('year', 'make', 'data') documented with example values. The description adds minimal semantic value beyond the schema, as it only reiterates that parameters are 'by a given year and make' and mentions 'data' implicitly. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for vehicle maintenance API by a given year and make.' It specifies the verb ('provides a list'), resource ('models'), and context ('for vehicle maintenance API'), which is clear and specific. However, it doesn't explicitly distinguish this tool from sibling tools like 'models', 'models_2', etc., which appear to be similar, so it doesn't reach the highest score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 the context ('for vehicle maintenance API') but doesn't specify when to choose this tool over sibling tools such as 'models' or 'decode', nor does it outline any prerequisites or exclusions. This lack of comparative usage information limits its effectiveness for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'Provides a list', which suggests a read-only operation, but doesn't clarify if it's safe, requires authentication, has rate limits, or what the output format looks like. For a tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any fluff. Every word contributes directly to understanding the tool's function, making it appropriately sized and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the returned list contains (e.g., model names, IDs), any behavioral traits like error handling or pagination, or how it fits among sibling tools. For a tool with 2 required parameters and no structured support, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('year' and 'make') documented in the schema. The description adds minimal value beyond the schema by mentioning these parameters in context ('by a given year and make'), but doesn't provide additional semantics like format examples or constraints. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for Advanced Decode API by a given year and make.' It specifies the verb ('Provides a list'), resource ('models'), and scope ('by a given year and make'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'model', 'models', or 'models_3', which prevents 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'by a given year and make', but it provides no explicit guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'model', 'models', 'models_3'), there's no indication of how this tool differs or when it's preferred, leaving the agent to guess based on naming alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list, implying a read-only operation, but does not cover critical aspects like authentication requirements, rate limits, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the main purpose, making it easy to understand quickly. However, it could be slightly more structured by explicitly listing all parameters or usage contexts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It does not explain the return values (e.g., list format, data structure), error conditions, or behavioral nuances like pagination or dependencies. For a tool with three parameters and no structured output information, more descriptive context is needed to ensure reliable agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions parameters ('by a given year and make'), which aligns with the input schema properties 'year' and 'make'. However, it omits the 'data' parameter entirely, which is required. With 100% schema description coverage, the schema already documents all parameters, so the description adds minimal value beyond restating two of them, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for vehicle warranty API by a given year and make.' It specifies the verb ('Provides a list'), resource ('models'), and scope ('for vehicle warranty API'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'models', 'models_2', 'models_4', etc., which appear to be similar, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 the context ('for vehicle warranty API') but does not specify prerequisites, exclusions, or compare it to sibling tools such as 'model', 'models', or 'models_arket_value'. Without this, users must infer usage from the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list, implying a read-only operation, but doesn't mention any behavioral traits such as rate limits, authentication requirements, error handling, or response format. For a tool with zero annotation coverage, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured by separating usage context from the action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 required parameters, no output schema, and no annotations), the description is incomplete. It doesn't cover behavioral aspects like response format, error cases, or usage constraints, and it lacks differentiation from sibling tools. For a tool in a crowded namespace with no structured support, this leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters documented in the schema. The description adds minimal value beyond the schema by implying that 'year' and 'make' are used to filter models, and 'data' relates to 'repair', but it doesn't explain parameter interactions or provide additional context. This meets the baseline score of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for vehicle repairs API by a given year and make.' It specifies the verb ('Provides a list'), resource ('models'), and context ('for vehicle repairs API'), distinguishing it from generic model-listing tools. However, it doesn't explicitly differentiate from sibling tools like 'models', 'models_2', etc., which likely serve similar purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'by a given year and make' but doesn't specify prerequisites, exclusions, or compare it to other model-related tools in the sibling list (e.g., 'models', 'models_2', 'models_5'). This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list but doesn't describe output format, pagination, error handling, rate limits, or authentication needs. For a tool with no annotation coverage, this is a significant gap in transparency about how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core function without unnecessary words. It's front-loaded with the main purpose. However, it could be slightly more structured by explicitly naming the parameters or output, but it remains highly concise and effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 required parameters, no nested objects) and 100% schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it lacks details on behavioral aspects like return format or error conditions. For a simple lookup tool, it meets basic needs but doesn't provide full context for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with both parameters ('year' and 'make') documented in the schema. The description mentions these parameters ('by a given year and make') but doesn't add any semantic details beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for vehicle recalls API by a given year and make.' It specifies the verb ('Provides a list'), resource ('models'), and context ('for vehicle recalls API'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'models', 'models_2', etc., which appear to serve similar functions, preventing 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., 'models', 'model', 'makes') or provide context for choosing this specific tool over others in the list. This lack of comparative information leaves the agent without clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list but doesn't describe any behavioral traits—such as whether it's a read-only operation, what the output format looks like (e.g., list of strings or objects), if there are rate limits, or if it requires authentication. For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without any fluff. It's front-loaded with the main action ('Provides a list'), making it easy to parse. Every word earns its place, and there's no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a lookup tool with two parameters) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the output contains (e.g., model names, IDs, or additional data), how results are structured, or any error conditions. For a tool in a crowded namespace with many siblings, more context is needed to ensure the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions parameters ('by a given year and make'), which aligns with the input schema's two required parameters. Since schema description coverage is 100% (with descriptions like 'Example value: 1999'), the schema already documents the parameters adequately. The description adds no additional semantic context—such as format expectations (e.g., year as string vs. integer) or usage notes—beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for market value API by a given year and make.' It specifies the verb ('provides a list'), resource ('models'), and context ('market value API'), distinguishing it from generic model-listing tools like 'models' or 'models_2'. However, it doesn't explicitly differentiate from similar market-value-related tools like 'models_market_value' (if that's a different sibling) or 'market_value_by_ymm', which slightly limits clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With many sibling tools like 'models', 'market_value_by_vin', and 'market_value_by_ymm', there's no indication of when this specific tool is appropriate—for example, whether it's for pre-filtering before valuation or for general model lookup. This leaves the agent to guess based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'provides a list' but doesn't clarify if this is a read-only operation, what the output format might be (e.g., JSON list, paginated), or any potential limitations (e.g., rate limits, authentication needs). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the core functionality without unnecessary details. It front-loads the key action ('provides a list') and specifies the resource and parameters clearly, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool with 3 required parameters and no output schema, the description is incomplete. It lacks information on return values, error handling, or behavioral traits, which are crucial for an agent to use the tool effectively. Without annotations or an output schema, the description should compensate more to provide a complete picture, but it falls short.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter ('year', 'make', 'model') documented in the input schema. The description adds minimal value beyond the schema by implying these parameters filter the trim list, but it doesn't provide additional context like valid formats or examples beyond what's in the schema. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of trims available for electric vehicle specifications API by a given year, make, and model.' It specifies the verb ('provides a list'), resource ('trims'), and scope ('electric vehicle specifications API'), distinguishing it from siblings like 'trims_2' or 'trims_3' which might have different scopes. However, it doesn't explicitly differentiate from 'multiple_trims' or other trim-related tools, keeping it at a 4 rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 when to choose 'trim' over 'trims_2', 'trims_3', or 'multiple_trims', nor does it specify any prerequisites or exclusions. Without such context, the agent must infer usage from the tool name and parameters alone, which is insufficient for clear decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes the tool as an API that 'provides details', implying a read-only operation, but lacks information on error handling, rate limits, authentication needs, response format, or data sources. This is a significant gap 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that efficiently convey the tool's purpose and geographic limitation. It is front-loaded with the core functionality, though it could be slightly more structured by explicitly listing the outputs or usage scenarios.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., error handling, rate limits), output format, and differentiation from sibling tools. For a tool with 2 parameters and many similar alternatives, this leaves significant gaps for an AI agent to understand its full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('license_plate' and 'state') documented in the schema. The description adds minimal value by mentioning the inputs ('taking license plate number and state') and clarifying the geographic scope ('only USA states'), but does not provide additional semantics beyond what the schema already covers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'provides details about year, make, model and VIN by taking license plate number and state', specifying the verb ('provides details'), resource ('year, make, model and VIN'), and inputs. It distinguishes itself by focusing on USA states, but does not explicitly differentiate from similar sibling tools like 'by_plate_and_state' or 'decode'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some context by stating 'This API covers only USA states', which implies a usage boundary. However, it offers no explicit guidance on when to use this tool versus alternatives like 'by_plate_and_state' or 'decode', nor does it mention prerequisites or exclusions beyond the geographic scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes what data is returned (recall details) but lacks critical behavioral information: authentication requirements (though 'x-Authkey' is in schema), rate limits, error handling, pagination, or whether it's read-only/destructive. The mention of 'API' suggests it's a read operation, but this isn't explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences. The first sentence front-loads the core purpose and key return data. The second adds coverage details. There's no wasted text, though it could be slightly more structured (e.g., bullet points for return fields).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a parameter that doesn't specify the vehicle (only auth), the description is incomplete. It mentions return data but doesn't detail the output structure or how to identify the vehicle (VIN parameter missing). For a recall tool with many siblings, more context on uniqueness and usage is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with one parameter ('x-Authkey') documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema—it doesn't explain how to specify the vehicle (e.g., via VIN) or clarify the auth key usage. Baseline 3 is appropriate since the schema covers the parameter, but the description doesn't compensate for missing vehicle identification details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'provides details on all the open recalls of any vehicle' with specific information like date, recall ID, and remedy. It distinguishes itself from siblings by focusing on recalls rather than decoding, market value, or other vehicle data. However, it doesn't explicitly contrast with similar tools like 'title_check' or 'stolen_check' that might also involve vehicle safety/status checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions coverage for VINs from 1952-2023, which implies when the tool is applicable, but offers no explicit advice on when to use this vs. alternatives like 'by_vin' or other decode tools. There's no mention of prerequisites, error conditions, or comparison to sibling tools for recall-specific queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states this is an information-providing API and mentions coverage years (1999-2023), but doesn't describe important behavioral aspects like response format, error handling, authentication requirements, rate limits, or whether it's a read-only operation. The description is insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise with three sentences that each serve a purpose: stating the tool's function, listing warranty types covered, and specifying coverage years. It's front-loaded with the core purpose. However, the second sentence about warranty types could be more efficiently integrated, and there's some redundancy in mentioning parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that presumably returns complex warranty information. It mentions warranty types and coverage years but doesn't describe the structure of returned data, what specific information is provided about each warranty type, or how results are formatted. For a data retrieval tool with no structured output documentation, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (year, make, model). The description adds minimal value by mentioning these parameters ('when called by year, make and model') but doesn't provide additional semantic context beyond what the schema provides. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'provides information on the type and length of any vehicles warranty when called by year, make and model.' It specifies the verb ('provides information'), resource ('warranty'), and scope ('vehicles'). However, it doesn't explicitly differentiate from sibling tools like 'vehicle_maintenance' or 'vehicle_recall' that also provide vehicle-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 coverage for manufacturers from 1999 to 2023, which is a constraint but not usage guidance. There's no mention of when to choose this over sibling tools like 'by_vin' or 'decode' for warranty information, or what makes this tool the appropriate choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'reads' and 'returns' data, implying a read-only operation, but lacks details on error handling, rate limits, authentication needs, or performance traits (e.g., accuracy, supported image formats). The mention of 'Support: 17 digit VIN' adds some context but is insufficient for a mutation-free tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core functionality in the first sentence. Both sentences earn their place by specifying the action and a key constraint ('17 digit VIN'). There's no unnecessary verbosity, making it efficient, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (image-based VIN reading), no annotations, and no output schema, the description is minimally complete. It covers the basic purpose and input type but lacks details on output format, error cases, or integration context. This is adequate for a simple read tool but leaves gaps in behavioral and usage aspects, scoring as a baseline viable description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the two parameters ('file' and 'url') with basic descriptions. The description adds no parameter-specific semantics beyond implying image input, which is covered by the schema. This meets the baseline of 3, as the schema handles parameter documentation adequately without extra value from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'reads a VIN number from... any image' and 'returns the VIN number.' It specifies the verb ('reads'), resource ('VIN number'), and input type ('image'). However, it doesn't explicitly differentiate from sibling tools like 'license_plate_ocr' or 'by_vin', which handle different inputs or methods, leaving some ambiguity in sibling context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance: it mentions the tool is for reading VINs from images, but offers no explicit advice on when to use it versus alternatives (e.g., 'by_vin' for non-image inputs or 'license_plate_ocr' for plates). There's no mention of prerequisites, exclusions, or comparative contexts, relying solely on the stated purpose without deeper guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While it mentions the data scope (1999-2023) and types of data returned, it doesn't disclose important behavioral aspects: whether this is a read-only operation, what happens with invalid parameters, rate limits, authentication requirements, or what format the response takes. For a data retrieval tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality. It's appropriately sized for a straightforward lookup tool, though it could be slightly more structured by separating scope from data types. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 required parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what happens when parameters don't match existing vehicles, what the response structure looks like, or how to interpret the 'etc.' in the data listing. The agent would need to guess about important operational aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four required parameters (year, make, model, trim). The description doesn't add any parameter-specific information beyond what's implied by the tool's purpose. It doesn't explain parameter formats, constraints, or relationships between them. Baseline 3 is appropriate when schema does the documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving vehicle data (specs, engine details, transmission, etc.) for a specific vehicle from 1999-2023. It specifies the resource (vehicle data) and scope (1999-2023), but doesn't explicitly differentiate from sibling tools like 'decode_by_ymmt' or 'market_value_by_ymm' that might also use year/make/model parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. With many sibling tools available (like decode_by_ymmt, market_value_by_ymm, universal_vin_decode), the description doesn't indicate whether this is the primary lookup tool for YMMT queries or when other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions coverage scope and VIN format support, but doesn't describe what the API returns (e.g., list of sales events, timestamps, prices), error conditions, rate limits, authentication requirements, or whether it's a read-only operation. The description is insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that each add value: purpose statement, coverage scope, and VIN format details. It's front-loaded with the core functionality and avoids unnecessary fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what data is returned (sales events, dates, prices), error handling, or behavioral characteristics. While it covers input semantics well, it leaves critical gaps about the tool's behavior and output that an agent would need to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with a clear parameter description for 'vin', including an example. The description adds value by specifying VIN format support (17-digit and classic 5-13 digit VINs) and geographic coverage (North America), which provides important context beyond the schema's technical specification.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to uncover sales history of a vehicle using a VIN, with coverage in North America. It specifies the verb ('uncovering') and resource ('sales history'), but doesn't explicitly differentiate from sibling tools like 'auction', 'market_value_by_vin', or 'title_check' that might also provide vehicle-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some context about coverage (North America) and VIN support, but offers no explicit guidance on when to use this tool versus alternatives like 'by_vin', 'market_value_by_vin', or 'title_check'. It doesn't mention prerequisites, limitations, or comparative use cases with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 the tool 'returns' data and is 'comprehensive and accurate', but lacks details on error handling, rate limits, authentication needs, response format, or whether it's read-only or has side effects. For a tool with no annotations, this leaves significant behavioral gaps, though it does specify coverage and target audience.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long and front-loaded with the core purpose, but includes some marketing language ('designed for customers and businesses', 'deliver a complete picture') that doesn't add operational value. It could be more concise by focusing strictly on functional details, though it's not excessively verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description provides basic purpose and scope but lacks details on behavior, output structure, and differentiation from siblings. It's minimally adequate for a simple lookup tool but doesn't fully address the context needed for optimal agent use, especially without annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single parameter 'vin', including an example. The description doesn't add any parameter-specific information beyond what's in the schema, such as VIN format requirements or validation rules. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'USA VIN decode is built to return a wide range of data points' and 'returns comprehensive and accurate VIN descriptions, packages information, and more.' It specifies the verb ('decode'), resource ('VIN'), and scope ('USA', '1981-present'), distinguishing it from some siblings like 'eu_decode' or 'motorcycle_decode'. However, it doesn't explicitly differentiate from similar tools like 'by_vin', 'vin_decode', or 'universal_vin_decode', which slightly reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: 'designed for customers and businesses in need of complete vehicle specifications, features, and equipment' and 'Coverage: 1981-present', suggesting it's for detailed USA vehicle data. However, it doesn't explicitly state when to use this tool versus alternatives like 'by_vin' or 'vin_decode', nor does it provide exclusions or prerequisites. The guidance is present but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes a read-only operation ('provides a list'), which is straightforward, but it doesn't mention any behavioral traits such as rate limits, authentication needs, error handling, or pagination. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality. There's no wasted verbiage or redundancy, making it easy for an agent to parse quickly. Every word earns its place by conveying essential information without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects, no output schema) and 100% schema coverage, the description is adequate but not fully complete. It lacks details on output format, error cases, or how it differs from sibling tools, which could help an agent use it more effectively. However, for a simple list-providing tool, it meets minimal expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both parameters ('year' and 'data') with descriptions. The description adds minimal value beyond the schema by implying the 'year' parameter is used for filtering makes and 'data' might relate to 'repair', but it doesn't provide additional syntax, format details, or examples. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of makes available for vehicle repairs API by year.' It specifies the verb ('provides'), resource ('list of makes'), and context ('vehicle repairs API by year'). However, it doesn't explicitly differentiate from sibling tools like 'make', 'makes', 'makes_2', etc., which likely have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It implies the tool should be used when needing makes for vehicle repairs by year, but it doesn't specify when to use this tool versus alternatives like 'makes', 'makes_2', or 'makes_5'. No exclusions, prerequisites, or explicit alternatives are mentioned, leaving the agent with little direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes a read-only list operation, which implies safety, but does not mention potential limitations like rate limits, authentication needs, error handling, or the format of the returned list. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It uses clear language and avoids redundancy, making it easy to parse quickly. Every word contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 required parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameters but lacks details on usage guidelines, behavioral traits, and output expectations. For a list tool in a context with many similar siblings, more guidance would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with parameters 'year' and 'make' documented as strings. The description adds context by specifying these are for 'YMM specifications API', but does not provide additional semantics like valid formats, examples, or constraints beyond what the schema implies. With high schema coverage, the baseline score of 3 is appropriate as the description adds minimal extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of models available for YMM specifications API by a given year and make.' It specifies the verb ('Provides a list'), resource ('models'), and context ('YMM specifications API'), but does not explicitly differentiate from sibling tools like 'models', 'models_2', etc., which likely serve similar purposes. This makes it clear but not fully distinguished from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 the context ('YMM specifications API') but does not specify prerequisites, exclusions, or compare it to sibling tools such as 'models' or 'models_6'. Without this, users must infer usage from the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 retrieves a list, implying a read-only operation, but does not specify whether it requires authentication, has rate limits, returns paginated results, or handles errors. For a tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that is front-loaded with the core purpose. It wastes no words and is appropriately sized for a simple lookup tool, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameters, but lacks details on behavior, output format, or error handling. For a read-only tool with no complex traits, it is complete enough to be functional but leaves gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions the parameters ('by year, make, and model'), but the input schema already has 100% description coverage, with each parameter documented (though descriptions are minimal with 'Example value: '). The description adds no additional meaning beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the trims list by year, make, and model.' It specifies the verb ('Get') and resource ('trims list'), and includes the required parameters. However, it does not explicitly distinguish this tool from sibling tools like 'trim', 'trims_2', or 'trims_3', which appear to have similar functions, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 does not mention any context, prerequisites, or exclusions, and does not reference sibling tools like 'trim' or 'multiple_trims' that might serve similar purposes. Usage is implied only by the parameters, with no explicit instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes a read-only list operation, which is straightforward, but doesn't disclose behavioral traits like pagination, rate limits, error conditions, or response format. The description is minimal and lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (simple lookup), 100% schema coverage, and no output schema, the description is minimally adequate. However, with no annotations and many sibling tools, it lacks differentiation and operational details that would enhance completeness for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the three required parameters (year, make, model). The description adds no additional meaning beyond implying these are used to filter trims, matching the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of trims available for Advanced Decode API by a given year, make, and model.' It specifies the verb ('provides a list'), resource ('trims'), and scope ('Advanced Decode API'), but doesn't explicitly differentiate from sibling tools like 'trims', 'trims_3', or 'multiple_trims'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'by a given year, make, and model', which implies usage context, but provides no explicit guidance on when to use this tool versus alternatives like 'trims', 'trims_3', or 'multiple_trims'. No exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes a read-only operation ('provides a list'), which implies non-destructive behavior, but fails to mention any constraints like rate limits, authentication needs, error handling, or pagination. For a tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and parameters, making it easy to parse. There is no redundancy or fluff, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a simple query with three parameters), no annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and parameters but lacks details on behavior, output format, and usage context. While it meets minimum viability, it does not fully compensate for the absence of structured data, leaving room for improvement in guiding the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with all three parameters ('year', 'make', 'model') documented in the schema itself. The description adds minimal value by implying these parameters are used to query the API, but it does not provide additional context such as format examples, validation rules, or semantic meaning beyond what the schema already states. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of trims available for YMM specifications API by a given year, make, and model.' It specifies the verb ('provides a list'), resource ('trims'), and scope ('YMM specifications API'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'trim', 'trims', or 'trims_2', which appear related, so it falls short of 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.
Usage Guidelines2/5
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 does not mention any prerequisites, exclusions, or comparisons to sibling tools such as 'trim', 'trims', or 'multiple_trims', leaving the agent without context for tool selection. This lack of usage guidelines reduces its effectiveness in a multi-tool environment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list, implying a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or the format of the returned list. For a tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and efficiently conveys the essential information, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's apparent simplicity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage context, behavioral traits, or output format. For a tool in a complex server with many siblings, more context would be beneficial, but it meets the minimum viable threshold.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter ('data') with 100% description coverage in the schema ('Example value: warranty'). The description adds no additional meaning about parameters, such as explaining what 'data' represents or how it affects the output. With high schema coverage, the baseline is 3, as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for vehicle warranty year.' It specifies the action ('provides a list'), resource ('years'), and context ('for vehicle warranty year'), which is clear and specific. However, it doesn't explicitly distinguish this tool from sibling tools like 'year_2', 'years', or 'years_2', which might offer similar year-related functionality, 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.
Usage Guidelines2/5
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 any prerequisites, exclusions, or specific contexts for usage. Given the many sibling tools (e.g., 'year_2', 'years', 'vehicle_warranty'), this lack of differentiation leaves the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list, implying a read-only operation, but doesn't cover other aspects like authentication needs, rate limits, error handling, or what the output looks like (e.g., format, pagination). For a tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and efficient, making it easy for an agent to parse quickly. Every part of the sentence contributes to understanding the tool, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does but lacks details on usage guidelines, behavioral traits, and output format. For a basic list-providing tool, this is minimally viable, but it could be more complete to aid the agent fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter 'data' described as 'Example value: maintenance'. The description doesn't add any meaning beyond this, as it doesn't explain what 'data' represents or how it relates to the tool's function. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for vehicle maintenance API.' It specifies the verb ('provides a list'), resource ('years'), and context ('for vehicle maintenance API'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'year', 'years', or 'years_3', which might have similar functions, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 any prerequisites, context for usage, or comparisons to sibling tools like 'year' or 'years', leaving the agent to infer usage based on the name and description alone. This lack of explicit guidelines reduces its helpfulness.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 a read operation ('Get'), but doesn't mention any behavioral traits such as data format, potential errors, or performance considerations. This is inadequate for a tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded and directly states the purpose, making it highly concise and well-structured for its simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of sibling tools and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'YMMT' means, the format of the year list, or how it differs from similar tools, leaving significant gaps for an AI agent to understand its full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so no parameter information is needed. The description adds value by specifying 'for YMMT', which implies a context or scope, though it's not fully explained. Baseline is 4 for zero parameters, as it appropriately avoids redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'year list for YMMT', making the purpose understandable. However, it doesn't differentiate from sibling tools like 'year', 'year_2', 'years_2' through 'years_6', and 'years_market_value', which appear to serve similar functions, so it's not fully specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With many sibling tools that might overlap (e.g., 'year', 'years_2'), there's no indication of context, prerequisites, or exclusions, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes a read operation ('provides a list'), implying it's non-destructive, but doesn't mention rate limits, authentication needs, error handling, or output format. For a tool with zero annotation coverage, this is insufficient to inform the agent adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action and resource, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on usage, behavior, and output. For a basic list tool, it's functional but could be more informative to fully guide the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter 'data' described as 'Example value: repair'. The description doesn't add any meaning beyond this, such as explaining the parameter's role or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema handles the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for vehicle repairs API.' It specifies the verb ('provides'), resource ('list of years'), and context ('vehicle repairs API'), making it understandable. However, it doesn't distinguish from sibling tools like 'years', 'years_2', 'years_4', etc., which likely serve similar purposes, so it's not fully differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'vehicle repairs API' but doesn't specify prerequisites, exclusions, or compare to siblings such as 'years' or 'vehicle_repair'. This lack of context leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list, implying a read-only operation, but doesn't cover critical aspects like authentication requirements, rate limits, error handling, or response format. For a tool with zero annotation coverage, this minimal description leaves key behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
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 any fluff or redundancy. It's appropriately sized for a simple tool and front-loaded with the core functionality, making it easy for an agent to parse quickly. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimal but adequate for basic understanding. However, it lacks context about the API's scope (e.g., global vs. regional years, data freshness) and doesn't address sibling tool differentiation, which is crucial in this crowded namespace. For a tool with no structured support, more completeness would help the agent use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100% (as there are no parameters to describe). In such cases, the baseline score is 4, as there's no need for the description to compensate for parameter documentation. The description doesn't add parameter semantics, but that's unnecessary here, so it meets expectations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for electric vehicle Specifications API.' It specifies the verb ('provides'), resource ('list of years'), and domain context ('electric vehicle Specifications API'), which is clear and specific. However, it doesn't explicitly differentiate from sibling tools like 'year', 'year_2', 'years', 'years_2', etc., which likely serve similar or overlapping functions, preventing 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.
Usage Guidelines2/5
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. With multiple sibling tools related to years (e.g., 'year', 'years', 'years_2', 'years_3', 'years_4', 'years_5', 'years_market_value'), there is no indication of context, prerequisites, or distinctions, leaving the agent to guess based on names alone. This lack of usage guidance is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses coverage constraints (year range, region, VIN format) which is useful behavioral context. However, it doesn't mention error conditions, rate limits, authentication needs, or what happens with invalid VINs. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three concise sentences. It's front-loaded with the core purpose, followed by coverage and support details. No wasted words, though the structure could be slightly improved by combining related coverage information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter lookup tool with no output schema, the description provides adequate purpose and input constraints. However, it doesn't describe the return format or structure of the 'basic specifications', which would be helpful given the lack of output schema. The coverage details are good, but more behavioral context would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single parameter 'vin' that has an example value. The description adds context about VIN requirements (17 digits, US/Canada region, 1981-2024 range) which provides semantic meaning beyond the schema's example. This meets the baseline expectation when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'provides year, make, model, series, and basic specifications of a motorcycle' from a VIN. It specifies the resource (motorcycle) and output fields, but doesn't explicitly differentiate from sibling tools like 'decode', 'vin_decode', or 'by_vin' which appear to serve similar functions for broader vehicle types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through coverage details ('VIN from 1981 to 2024', '17 digit VINs based on US and Canada region'), which helps determine when this tool is applicable. However, it doesn't explicitly state when to use this versus alternatives like 'decode' or 'by_vin', nor does it provide exclusion criteria beyond the implied motorcycle focus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the return content (salvage record status, date, cause) and coverage scope, but does not disclose critical behavioral traits such as authentication requirements, rate limits, error handling, or whether this is a read-only operation. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with two sentences that efficiently convey purpose and scope. There is no unnecessary information, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description provides basic purpose and scope but lacks details on behavioral aspects like permissions or error handling. It is minimally adequate but has clear gaps that could hinder effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'vin' documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as VIN format constraints or validation rules. Baseline score of 3 is appropriate since the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 title information of any vehicle with VIN' and specifies what it returns ('if the vehicle has salvage record or not on a particular date along with cause'). It distinguishes itself from siblings like 'stolen_check' or 'vin_decode' by focusing on title/salvage status, but could be more explicit about differentiation from similar tools like 'by_vin' or 'by_vin_2'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'Coverage: All vehicles listed in North America,' which helps determine applicability. However, it lacks explicit guidance on when to use this tool versus alternatives like 'by_vin' or 'stolen_check,' and does not mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions regional coverage (US/Canada) and date range (1981-2024), which adds useful context. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or what happens with invalid VINs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences. The first sentence states the purpose clearly, and the second adds important constraints. There's no wasted text, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 1 parameter with 100% schema coverage but no annotations and no output schema, the description provides adequate basic information about what the tool does and its constraints. However, for a tool with no annotations or output schema, it should ideally provide more behavioral context about what to expect from the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with the parameter 'vin' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. With high schema coverage, the baseline is 3 even without additional parameter semantics in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'provides basic specifications including year, make, model, trim, engine specs, drivetype and fuel types of any vehicle.' It specifies the verb ('provides') and resource ('vehicle specifications'), but doesn't explicitly distinguish it from similar sibling tools like 'decode', 'vin_decode', or 'by_vin'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating it 'Works for all 17 digit VINs based on US and Canada regions and having coverage from 1981 to 2024.' However, it doesn't explicitly say when to use this tool versus alternatives like 'decode', 'vin_decode', or 'by_vin', nor does it provide any exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 that the decode is 'premium' and provides 'detailed specifications,' but does not disclose critical traits such as rate limits, authentication needs, error handling, or what happens with invalid VINs. The description adds some context about coverage and support, but lacks comprehensive behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. However, it includes some redundant phrasing (e.g., 'more than just a basic decode' could be more direct) and the sentences about coverage and support, while useful, could be integrated more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (single parameter, no output schema, no annotations), the description is moderately complete. It covers the purpose, scope, and input requirements, but lacks details on output format, error cases, or performance characteristics. Without an output schema, more information on return values would be beneficial for full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'vin' documented in the schema as a string with an example. The description adds no additional meaning beyond the schema, such as format constraints or validation rules, but since the schema already covers the parameter adequately, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Premium VIN decode' that provides 'detail information about a vehicle including detailed specifications.' It specifies the verb ('decode') and resource ('vehicle information'), but does not explicitly differentiate from sibling tools like 'by_vin', 'decode_by_vin', or 'universal_vin_decode', which appear to offer similar functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating coverage for 'VIN from 1981-Present' and support for '17 digit VIN number,' which suggests when to use it (for VINs in that range and format). However, it does not explicitly state when to choose this tool over alternatives like 'by_vin' or 'decode_by_vin', nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions the tool's function (error detection and suggestion) and constraints (year range, digit length), but doesn't describe what happens when errors are detected, how suggestions are presented, whether this is a read-only operation, or any rate limits or authentication requirements. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that each serve a purpose: stating the core functionality, specifying coverage, and stating support constraints. It's front-loaded with the main purpose. However, the second sentence could be more smoothly integrated, and there's some minor grammatical awkwardness ('This API supports VIN from year').
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (error detection and correction), no annotations, no output schema, and 100% schema coverage for a single parameter, the description provides basic completeness but has significant gaps. It covers what the tool does and some constraints, but doesn't explain the output format, error handling, or how suggestions are structured, which would be important for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for its single parameter ('vin'), so the schema already documents it adequately. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'detects the error in the VIN digits and provides suggestions for the correct VINs including year, make and model.' It specifies the verb (detects error, provides suggestions) and resource (VIN digits), but doesn't explicitly differentiate from sibling tools like 'vin_decode' or 'decode_by_vin' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context by specifying coverage (VINs from 1982-present) and support (17-digit VINs), which implies when to use this tool. However, it doesn't explicitly state when to choose this tool over alternatives like 'vin_decode' or 'decode_by_vin', nor does it mention prerequisites or exclusions beyond the year and digit constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 that the tool provides a list, implying a read-only operation, but does not specify any behavioral traits such as rate limits, authentication requirements, or what the output format looks like. For a tool with zero annotation coverage, 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.
Conciseness5/5
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: 'Provides a list of years available for Advanced Decode API.' It is front-loaded with the core purpose and contains 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has zero parameters and no output schema, the description adequately covers the basic purpose. However, it lacks details on behavioral aspects (e.g., output format, any constraints) and does not differentiate from sibling tools, which reduces completeness. It meets the minimum viable standard but has clear gaps in providing full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema description coverage is 100%, so there is no need for parameter details in the description. The description appropriately focuses on the tool's purpose without redundant parameter information, earning a high score as it adds value by explaining what the tool does rather than repeating schema details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for Advanced Decode API.' It specifies the verb ('Provides a list') and resource ('years'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'year', 'year_2', 'years', 'years_3', etc., which might offer similar year-related data, so it falls short of 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. With multiple sibling tools related to years (e.g., 'year', 'years', 'years_3'), there is no indication of what makes 'years_2' distinct or when it should be preferred. This lack of context leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'provides a list' but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or response format. For a tool with zero annotation coverage, 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.
Conciseness5/5
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 is appropriately sized and front-loaded, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no annotations, and no output schema, the description is minimal but adequate for a simple list-providing tool. However, it lacks details on output format or behavioral context, which could be helpful. It meets the minimum viable standard but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%. The description does not need to add parameter semantics, as there are none to document. A baseline score of 4 is appropriate since no parameters exist, and the schema fully covers this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for YMM specifications API.' It specifies the verb ('provides a list') and resource ('years'), but does not explicitly differentiate from sibling tools like 'year', 'years', 'years_2', etc., which likely serve similar purposes. This makes it clear but not fully distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With many sibling tools (e.g., 'year', 'years', 'years_2', 'years_3', 'years_5', 'years_6'), there is no indication of context, prerequisites, or differences, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 provides a list, implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or the format of the returned list. For a tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core function without unnecessary details. It's front-loaded with the main action and resource, making it easy to parse. There's no wasted verbiage, and every word contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on the return format, error conditions, or integration with sibling tools. For a basic list-fetching tool, it meets the bare minimum but doesn't provide full context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain any parameters, which is appropriate. It implicitly suggests no inputs are required to fetch the list of years, aligning with the schema. A baseline of 4 is given since no parameters exist, and the description doesn't add or contradict parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for vehicle recall API.' It specifies the action ('provides a list'), resource ('years'), and context ('for vehicle recall API'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'years', 'years_2', etc., which likely serve similar purposes, preventing 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing to call this before other recall-related tools, or compare it to siblings like 'years' or 'vehicle_recall'. Without any usage context, the agent must infer when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool provides a list, implying a read-only operation, but doesn't specify if it's static or dynamic data, requires authentication, has rate limits, or details the return format. This leaves significant gaps for a tool that likely fetches API metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's function without any fluff. It's front-loaded with the core purpose, making it highly efficient and easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema), the description is minimally adequate. It explains what the tool does but lacks behavioral context like data format or usage constraints. Without annotations or output schema, more detail on the return value would improve completeness for a list-providing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, avoiding redundancy. A baseline of 4 is applied since it doesn't add unnecessary details beyond the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Provides a list of years available for market value API.' It specifies the verb ('Provides a list') and resource ('years available for market value API'), making it distinct from siblings like 'years' or 'years_2' which lack the market value context. However, it doesn't explicitly differentiate from 'years_market_value' itself, as there's no direct sibling with identical focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing market value data access, or compare it to similar tools like 'years' or 'market_value_by_vin', leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the tool 'provides a list' but doesn't disclose behavioral traits like whether it's read-only, pagination behavior, error conditions, rate limits, or authentication requirements. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the core purpose without unnecessary words. It's appropriately sized for a simple lookup tool and front-loads the key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter lookup tool with no output schema and no annotations, the description provides basic purpose but lacks important context about return format, error handling, or behavioral characteristics. It's minimally adequate but has clear gaps given the absence of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (year, make) clearly documented in the schema. The description adds minimal value beyond the schema by mentioning these are filtering parameters, but doesn't provide additional context like format expectations or examples beyond what's already in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Provides a list') and resource ('models available for vehicle warranty API'), specifying it's filtered by year and make. It distinguishes from generic 'models' tools by mentioning the warranty API context, but doesn't explicitly differentiate from other model-related siblings like 'models_market_value' or 'models_7'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (vehicle warranty API, filtering by year and make) but doesn't explicitly state when to use this tool versus alternatives like 'models_market_value' or other model-listing tools. No guidance on prerequisites or exclusions is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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