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Check Digits

by dev.toolstop

Check whether an identifier is mistyped

validate_identifier
Read-only

Verify a number when you already know what it is supposed to be. Use this whenever someone gives you a product barcode (GTIN, UPC or EAN), a book number (ISBN-10 or ISBN-13), a vehicle VIN, a US healthcare provider NPI, a security ISIN, or a legal entity LEI, and acting on a wrong one would cost something: a bounced claim, a rejected listing, a record that quietly corrupts a dataset.

Every supported format is a public identifier. Bank accounts, IBANs, routing numbers and payment cards are not supported and must not be sent.

Returns whether the checksum passes and, when it fails, the specific reason. If you do not already know the format, call identify_format first rather than guessing a kind, since a valid identifier checked against the wrong format comes back invalid. A passing checksum proves only that the digits are internally consistent: it does not mean the book, product, vehicle or provider exists, is active, or belongs to any particular person.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesWhich identifier format to validate against.
valueYesThe identifier to check. Spaces and dashes are ignored.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoBounded reason the check failed. Absent when valid.
validYesWhether the check digit is arithmetically consistent.
widthNoThe resolved width of a GTIN. GTIN input only.
reasonNoHuman-readable detail on the failure. Absent when valid.
countryNoISO country code parsed from the identifier. ISIN only.
normalizedNoThe input with spaces and dashes removed, upper-cased.
actualCheckDigitNoThe digit the VIN actually carries at position 9. VIN only.
expectedCheckDigitNoThe digit a VIN should carry at position 9. VIN only.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only provide readOnlyHint and openWorldHint. The description adds substantial context: it returns a specific failure reason, clarifies that a passing checksum proves only internal consistency (not existence/activity/ownership), and notes the risk of validating against the wrong format. This goes well beyond the annotations.

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 three paragraphs, but every sentence carries meaningful information: purpose, exclusions, return behavior, and caveats. It is well-organized and front-loaded with the core action, though slightly longer than strictly necessary.

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?

Given the tool's complexity (validation across many formats), the description covers all critical aspects: supported formats, excluded formats, when to use an alternative tool, return value semantics, and limitations. The presence of an output schema further reduces the need to describe return structure, making this fully complete for an agent.

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 coverage is 100% for both value and kind, so the baseline is 3. The description adds value beyond the schema by warning against guessing 'kind' and pointing to identify_format for unknown formats, which clarifies correct use of the parameter. It doesn't add much about 'value' beyond what the schema mentions (spaces/dashes ignored), but the kind guidance elevates it.

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 opens with a specific action ('Verify a number when you already know what it is supposed to be') and enumerates the exact identifier formats (GTIN, UPC, EAN, ISBN, VIN, NPI, ISIN, LEI). This clearly distinguishes it from the sibling identify_format, which is for unknown formats.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance (when a wrong identifier would be costly), lists unsupported formats that must not be sent (bank accounts, IBANs, etc.), and instructs to call identify_format first when the format is unknown. This is model behavior for usage differentiation.

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

A4.6/5.0
Disambiguation5/5

Each tool serves a distinct, non-overlapping purpose: compute_luhn_digit constructs a missing check digit, validate_identifier checks a known format, and identify_format determines an unknown format. No ambiguity exists between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with lowercase and underscores: compute_, identify_, validate_. This is perfectly predictable.

Tool Count5/5

Three tools is well-scoped for a focused check-digit utility. Each tool is necessary and distinct, covering the core operations without bloat.

Completeness4/5

The set covers validation, identification, and Luhn check-digit computation. A minor gap is that computation is only offered for Luhn, not for other formats like ISBN or GTIN, but the validation and identification tools cover those formats adequately.

Resources