Vehicle full report (bundle) — One call: decoded VIN specs + all open safety recalls + EPA fuel economy & annual cost + a buy/caution/walk verdict with reasoning. Fuses multiple NHTSA & EPA sources. JSON. Price: $0.25 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | 17-char VIN | |
| miles | No | odometer miles (optional) | |
| price | No | asking price USD (optional, enables verdict) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true and destructiveHint=false, so no contradiction exists. The description adds valuable behavioral context beyond annotations: it fuses multiple NHTSA & EPA sources, returns JSON, and carries a specific price of $0.25 USDC via x402. This goes beyond simple safety flags and helps an agent anticipate cost and external data dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. It front-loads the core identity ('Vehicle full report (bundle)') and the 'One call' value proposition, then delivers a scannable component list, output format, and pricing. Every clause earns its place.
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?
Without an output schema, the description compensates well by naming the report components, the JSON format, and the pricing. It also clarifies the all-in-one nature. Minor gaps remain, such as exact response structure or behavior on invalid VINs, but for a read-only lookup tool with fully documented parameters, the description is largely complete.
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 provides 100% coverage with descriptions for all three parameters, so the baseline is 3. The description reinforces that price enables the verdict and that the report includes fuel economy and annual cost, but it does not add new semantic detail beyond the schema, particularly for the optional 'miles' parameter.
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 deliverable ('Vehicle full report (bundle)') and enumerates concrete components: decoded VIN specs, open safety recalls, EPA fuel economy & annual cost, and a buy/caution/walk verdict. It also names the underlying sources (NHTSA & EPA), making it clearly distinguishable from siblings like vehicle_vin, 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 when to use the tool through 'bundle' and 'One call', suggesting it is the comprehensive option for vehicle research. However, it never explicitly states when not to use it or names alternatives such as vehicle_vin or vehicle_fuel_economy for narrower lookups. Usage context is implied rather than stated.
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.