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FabTally Valid

by io.github.zvmzaretsky

Decode a VIN via NHTSA vPIC (paid $0.002)

decode_vin

PAID $0.002 (x402, USDC on Base). Decode a Vehicle Identification Number: give {vin} and get a 17-char format + North-American check-digit validation and a full decode via NHTSA's public vPIC API — make, model, year, body class, engine, plant country and more. VIN-only (never plate→owner). Without payment returns the x402 challenge; pass x_payment to settle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYesA 17-character Vehicle Identification Number (excludes I, O, Q).
x_paymentNox402 payment payload (base64) for this PAID validation. If supplied it is forwarded as the X-PAYMENT header to settle the call and return the real result instead of a 402 challenge. Omit to get the price challenge first.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses the paid nature ($0.002, x402/USDC), the external API source (NHTSA vPIC), and the behavioral difference when x_payment is omitted (returns 402 challenge). This goes well beyond basic tool semantics.

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?

Three focused sentences, front-loaded with the payment warning, then the core function, then the payment behavior. Every sentence adds necessary information with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description enumerates the decode fields (make, model, year, body class, engine, plant country). The payment flow and VIN limitations are fully specified, making the tool's behavior predictable.

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?

Schema already describes both parameters, but the description adds value by explaining how x_payment is forwarded as the X-PAYMENT header and that omitting it yields the price challenge. This clarifies the parameter's role beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Decode a Vehicle Identification Number' and specifies the expected outputs (17-char format, check-digit validation, full decode via NHTSA vPIC). This differentiates it from sibling validation tools like validate_email or validate_vat.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on when to use the tool (VIN decoding) and an explicit exclusion ('VIN-only (never plate→owner)'). It also explains the payment workflow. However, it does not explicitly name alternative sibling tools for comparison.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct input type (VIN, place name, email, IBAN, VAT) with no overlap in purpose. An agent can easily distinguish decode_vin from validate_email based on the resource being processed.

Naming Consistency3/5

Three tools use the validate_* prefix (validate_email, validate_iban, validate_vat), but decode_vin and geo_lookup break the pattern with different verb/noun structures. The names are readable but not uniformly consistent.

Tool Count5/5

Five tools form a well-scoped validation utility set, covering common data types (VIN, email, IBAN, VAT, location) without redundancy or bloat. This is an ideal size for a focused server.

Completeness4/5

The set covers a solid range of common validation/lookup tasks, but typical additions like phone or credit-card validation are absent. These are minor gaps that do not hinder the core purpose.

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