CarScout
Server Details
Search used car inventory, check NHTSA recalls, decode VINs, and manage automated search scouts.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
The tools have distinct primary purposes: decode_vin for vehicle identification, check_recalls for safety data, and get_market_data for pricing and history. However, check_recalls and get_market_data both include recall data, which could cause minor confusion about which to use for recall information, though their broader focuses differ.
All tool names follow a consistent verb_noun pattern with clear, descriptive actions: decode_vin, check_recalls, and get_market_data. This uniformity makes it easy for an agent to predict and understand the tool set.
With 3 tools, the count is appropriate for a vehicle information server, covering key areas like identification, safety, and market data. It feels slightly thin but reasonable, as each tool serves a distinct and useful function without obvious bloat.
The tools cover core vehicle data needs—identification, safety recalls, and market info—but there are notable gaps. For example, there are no tools for creating, updating, or deleting vehicle records, and operations like searching for vehicles or comparing models are missing, which limits the server's utility for broader automotive tasks.
Available Tools
3 toolscheck_recallsCheck RecallsARead-onlyInspect
Look up NHTSA safety recalls and consumer complaints for a specific vehicle by make, model, and year. Returns recall campaign details and complaint severity data.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | Vehicle make (e.g. 'Toyota', 'Honda', 'BMW') | |
| year | Yes | Model year | |
| model | Yes | Vehicle model (e.g. 'RAV4', 'Civic', '3 Series') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint and openWorldHint, and the description aligns by indicating a read-only lookup. It adds value by specifying the nature of the returned data (recall campaign details and complaint severity data), beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, two sentences front-loaded with the purpose. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description informs about return content (recall details and complaint severity). For a simple tool with well-described parameters, it provides sufficient context for an agent to understand what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description mentions parameters conceptually (by make, model, and year) but does not add meaning beyond the schema's property descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it looks up NHTSA safety recalls and consumer complaints for a specific vehicle by make, model, and year, with specific output like recall campaign details and complaint severity data. It distinguishes from sibling tools like decode_vin and get_market_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when recall/complaint data is needed for a vehicle, but it does not explicitly state when to use it vs alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
decode_vinDecode VINARead-onlyInspect
Decode a Vehicle Identification Number (VIN) to get make, model, year, body style, engine, transmission, and other vehicle details.
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | The 17-character VIN to decode |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and openWorldHint. The description adds a list of expected return fields but does not elaborate on other behavioral traits such as error handling, rate limits, or the variable nature of 'other vehicle details'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the tool's purpose without extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema, the description adequately lists the expected vehicle details. It could mention validation of VIN format or error conditions, but it is sufficiently complete for a straightforward decode tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a description for the 'vin' parameter. The tool description adds no additional meaning beyond enumerating output fields, which are not parameter-related. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Decode' and resource 'Vehicle Identification Number (VIN)', and lists the details obtained (make, model, year, body style, engine, transmission, and other vehicle details). It clearly distinguishes from sibling tools like check_recalls and get_market_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when one has a VIN and wants vehicle details, but it does not explicitly state when to use this tool versus alternatives or mention any when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_market_dataGet Market DataARead-onlyInspect
Get current market pricing, recall, and fuel economy data for a specific vehicle by make, model, and year. Useful for understanding fair prices and vehicle history.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | Vehicle make (e.g. 'Toyota', 'Honda', 'BMW') | |
| year | Yes | Model year | |
| model | Yes | Vehicle model (e.g. 'RAV4', 'Civic', '3 Series') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations by listing the types of data returned (market pricing, recall, fuel economy). It does not contradict the readOnlyHint or openWorldHint annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences, front-loading the action and input details. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain the return format or structure of the data, nor does it mention pagination, error handling, or edge cases. Since there is no output schema, more detail is warranted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already describes each parameter clearly. The description merely restates the input parameters without adding new semantic depth.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'market pricing, recall, and fuel economy data' for a specific vehicle. It specifies the inputs (make, model, year) but does not explicitly differentiate from sibling tools like 'check_recalls' and 'decode_vin'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for understanding fair prices and vehicle history, but lacks explicit guidance on when to use this tool versus siblings. No exclusion criteria or alternatives are mentioned.
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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