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Check Digits

by dev.toolstop

Work out what an unlabeled number is

identify_format
Read-only

Identify a bare number whose type was never recorded. Tests it against every supported format and reports which ones its check digit satisfies. Use it for an unlabeled spreadsheet column, a value pulled out of a log line or a scanned document, or any number handed over without being named.

Reports every format that matches rather than choosing between them. Several matches is normal and does not mean the answer is unclear: some formats are subsets of others, so every ISBN-13 is also a valid EAN-13, and a short value can satisfy two unrelated formats by chance. Read one match as strong evidence and several as a set to narrow from context. As with validation, a match means the arithmetic is consistent, not that the identifier is registered or real.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesThe identifier to classify.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesThe normalized input that was tested.
matchedYesWhether any supported format matched.
matchesYesEvery format whose check digit the input satisfies, in registry order. Each entry carries `kind` plus the same format-specific fields validate_identifier returns for that format, such as `country` for an ISIN or `width` for a GTIN. Only satisfied formats are listed, so no entry carries `code` or `reason`.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations only provide readOnlyHint and openWorldHint. The description goes beyond these by explaining that the tool reports every matching format, that multiple matches are normal and due to format subsets or chance, and that a match only means arithmetic consistency, not real-world registration. This is valuable behavioral context not present in 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise, around 150 words. It front-loads purpose, then provides usage examples, then clarifies behavioral nuances. Every sentence earns its place—no fluff or repetition of schema fields.

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 moderate complexity, the presence of an output schema (so return values are covered), and annotations, the description is complete: it explains what it does, when to use it, how to interpret results, and the limitations of matches. It leaves no critical gaps for an agent to select and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (one parameter with a clear description). The description does not add extra parameter semantics beyond calling it an 'unlabeled number' and an 'identifier', which aligns with the schema. Baseline 3 is appropriate because the schema already fully covers the parameter.

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 the tool identifies a bare/unlabeled number by testing it against every supported format and reporting which check digit validates. This specific verb-resource pairing distinguishes it from siblings like validate_identifier (which presumably validates a known format) and compute_luhn_digit (which computes a digit).

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?

Provides explicit use cases: 'unlabeled spreadsheet column, a value pulled out of a log line or a scanned document, or any number handed over without being named.' This clearly indicates when to use the tool. It does not explicitly mention alternatives or when not to use it, but the 'unlabeled' framing plus sibling tool names imply validate_identifier is for known formats.

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