VIN decode + open recalls (NHTSA) — Decode any US-market VIN to full vehicle specs and list open safety recalls. Source: US NHTSA (federal, authoritative). JSON. Price: $0.005 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | 17-character VIN, e.g. 1HGCM82633A004352 |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and open-world, and the description adds useful behavioral context: the data source (US NHTSA), the JSON response format, the US-market scope, and the per-call price. It does not detail error behavior or response structure, but this is a simple read-only lookup and the annotations carry the safety profile.
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 compact and front-loaded with the core purpose, followed by source, format, and price. Every clause adds distinct information, with no redundancy or filler.
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?
With only one parameter, a well-covered schema, and annotations indicating read-only behavior, the description supplies enough context to invoke the tool correctly. It specifies what it returns (specs and recalls), the format (JSON), the source, and pricing. It does not explain error cases or response details, but for this simple API the missing pieces are minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already describes the vin parameter as a 17-character string with an example. The description adds meaningful domain semantics by limiting the tool to US-market VINs, which constrains the valid input space beyond the schema's generic string description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Decode') and resource ('US-market VIN') and explicitly names the two outputs: full vehicle specs and open safety recalls. The NHTSA reference distinguishes it from sibling vehicle tools like vehicle_fuel_economy and vehicle_deal_check.
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 a US-market VIN needs decoding and recall lookup, and it notes the NHTSA source and price. However, it does not explicitly contrast this tool with siblings like vehicle_report or vehicle_deal_check, leaving the agent to infer when this one is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.