Validates Luhn, IBAN, ISBN, EAN/UPC, SWIFT/BIC, and EU VAT checksums and formats.
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Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.1/5.
Each tool validates a completely distinct type of identifier (barcodes, IBANs, ISBNs, Luhn-based numbers, SWIFT/BIC codes, and EU VAT numbers). There is no overlap in purpose, and descriptions clearly differentiate the specific format and algorithm used.
All six tools follow an identical naming pattern: the lowercase identifier name followed by '_validate'. This consistent noun_verb structure makes it easy for an agent to predict the tool name for any identifier type.
With six tools covering the most common checksum-based identifiers (barcodes, IBANs, ISBNs, payment cards, SWIFT/BIC, EU VAT), the server is well-scoped. Each tool serves a clear purpose without unnecessary redundancy or excessive complexity.
The tool set covers the major standardized identifiers that rely on checksums or structural validation. While additional identifiers like VIN or SSN could be added, the current selection represents a natural and comprehensive set for a 'Checksum Validator' server.
ean_validateAInspectValidates an 8-digit (EAN-8), 12-digit (UPC-A), 13-digit (EAN-13), or 14-digit (GTIN-14) barcode checksum: alternating weights 1 3 applied right-to-left, check digit is (10 - sum mod 10) mod 10.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the checksum algorithm (alternating weights 1 3, right-to-left, check digit formula), which is helpful. However, it doesn't state whether the tool returns a boolean or throws errors, or what happens on invalid input format (e.g., non-numeric characters). The presence of an output schema mitigates this somewhat, but basic behavioral details are missing.
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 a single sentence that packs all critical information: supported barcode types, digit lengths, and the checksum algorithm. No wasted words, front-loaded with the core purpose. Excellent conciseness.
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?
Given the tool has a single required parameter, an output schema (not shown here but present), and 100% schema coverage, the description is largely complete. It explains what formats are valid and how the check works. A minor gap is not explaining return format or error behavior, but the output schema likely covers returns.
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 schema has 1 required parameter with 100% coverage (value is described as 'identifier or value to check'). The description adds significant meaning by specifying exactly what formats are accepted (EAN-8, UPC-A, etc.) and the validation algorithm, which goes beyond the schema's generic 'identifier or value to check' 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 clearly states it validates barcode checksums for EAN-8, UPC-A, EAN-13, and GTIN-14, with specific digit lengths. The verb 'validates' combined with the explicit resource ('barcode checksum') makes the purpose unambiguous and distinct from sibling tools like iban_validate or isbn_validate.
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 implicitly suggests when to use this tool—when validating barcode checksums—but provides no explicit guidance on when not to use it or when to prefer one of the sibling tools (e.g., luhn_validate for credit cards, iban_validate for IBANs). The list of supported barcode types is helpful but lacks exclusionary context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
iban_validateAInspectValidates an IBAN's length for its issuing country (per the SWIFT IBAN registry) and its mod-97 checksum. Does not decompose the BBAN into a bank code/account number or resolve a BIC.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully describes the tool's behavior: it validates specific criteria and does not perform other operations. It implicitly communicates non-destructive nature via 'validates'. Could be more explicit about side effects (none), but adequate.
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?
Two sentences, front-loaded with action. No redundant or vague language. Every word contributes to clarity. Second sentence efficiently states exclusions. Perfectly sized.
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?
Given the presence of an output schema (not shown), the description need not detail return values. It covers the validation logic (length and checksum) and explicitly states what is not done. For a simple validation tool with one input, this is complete and sufficient.
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 single parameter 'value' has a generic schema description ('identifier or value to check'). The tool description adds essential meaning by specifying that the value is an IBAN string, and that validation checks length and checksum. This compensates for the schema's lack of specificity.
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?
Clearly states the tool validates an IBAN's length and mod-97 checksum, and explicitly distinguishes from decomposition and BIC resolution. Differentiates from sibling tools like ean_validate, isbn_validate, and swiftbic_validate by specifying IBAN-specific validation.
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?
Provides clear context: validates length and checksum, but does not decompose or resolve BIC. This implies when to use (when IBAN format validation is needed) and when not (when further decomposition is needed). Lacks explicit naming of alternatives but is sufficient for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
isbn_validateAInspectValidates a 10- or 13-character ISBN. ISBN-10 uses weights 10..1 over 10 characters (last may be X, worth 10), sum mod 11 must be 0. ISBN-13 uses the same GTIN checksum as barcodes.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility. It discloses the checksum algorithms (ISBN-10 weights and modulo check, ISBN-13 GTIN check), which adds behavioral context beyond the schema. However, it does not mention error handling (e.g., what happens with non-numeric characters or incorrect length), return format, or side effects (though the tool is likely read-only). The presence of an output schema mitigates the need for return details, but lack of error behavior clarity leaves gaps.
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 extremely concise: two sentences. The first sentence states the core purpose, and the second provides the relevant algorithmic detail. No extraneous words, and all content is valuable. Information is front-loaded, making it easy for an agent to parse quickly.
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?
Given a single parameter with full schema coverage and the existence of an output schema (so return values are covered elsewhere), the description is nearly complete. It explains the validation logic in detail. A minor gap is lack of specification on whether hyphens or case variations (e.g., lowercase 'x') are accepted, but the algorithm description implicitly assumes pure digit/X strings. Overall, it provides sufficient context for a validation tool.
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 describes 'value' as an 'identifier or value to check' with 100% coverage. The description significantly enhances this by specifying that the value must be a 10- or 13-character ISBN, and explains the weighting and modulo logic. This adds critical validation constraints 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 explicitly states 'Validates a 10- or 13-character ISBN' with a specific verb (validates) and resource (ISBN), making the tool's purpose immediately clear. It also provides algorithmic details that distinguish it from sibling validation tools for other identifiers (EAN, IBAN, etc.).
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?
Usage is implied through the name and description but not explicitly stated. There is no 'when to use this vs alternatives' guidance, no exclusion criteria for invalid formats (e.g., hyphens), and no reference to sibling tools. The algorithm details provide enough context for an agent to infer the tool is for ISBN numbers, but explicit guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
luhn_validateBInspectValidates a numeric string against the Luhn (mod-10) checksum — payment card numbers, IMEI numbers, and similar identifiers.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavioral traits. It notes the algorithm (Luhn mod-10) and that the input should be a numeric string, but it does not state the output format (e.g., boolean true/false), error handling for invalid non-numeric input, or side effects. The presence of an output schema (context indicates one exists) partially mitigates this, but the description itself adds minimal behavioral context.
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 a single, well-constructed sentence that wastes no words. It front-loades the core function (validation against Luhn checksum) and provides relevant examples. Every part is purposeful.
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?
The tool is simple (one parameter, known algorithm), and an output schema exists (per context signals), so the description does not need to explain return values. However, it omits usage context relative to sibling tools (e.g., when to pick this over ean_validate) and does not mention any constraints (e.g., length limits for specific formats). The description is minimally adequate for a trivial tool but not fully informative.
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 description coverage is 100% (the sole parameter 'value' has a description). The tool description adds the context that the string should be numeric and aligns with the Luhn algorithm, which the schemas field lacks. This adds some meaning beyond the schema but does not compensate for missing details like expected length or allowed characters. Baseline 3 is appropriate.
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 clearly states the tool validates a numeric string using the Luhn (mod-10) checksum and gives specific examples (payment card numbers, IMEI numbers), making the purpose evident. However, it does not explicitly differentiate from sibling tools like ean_validate or isbn_validate, which also validate specific identifier formats, so a slight deduction applies.
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 that the tool is for Luhn-based identifiers but provides no explicit guidance on when to use it versus alternatives (e.g., ean_validate for EAN codes, iban_validate for IBANs). It lacks 'when-to-use' or 'when-not-to-use' language, leaving the agent to infer context from the examples alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
swiftbic_validateAInspectStructural format validation only for a SWIFT/BIC code: 8 or 11 characters (4 letters bank code + 2 letters country code + 2 alphanumeric location code + optional 3 alphanumeric branch code). The country code is not cross-checked against ISO 3166-1.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that only format validation is performed (not cross-checking against ISO 3166-1). It also specifies exact length and composition rules. Minor gap: does not mention case sensitivity or response format, but overall transparent for a simple format checker.
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?
Two sentences, zero wasted words. First sentence delivers purpose and format; second sentence clarifies limitation. Extremely efficient and front-loaded.
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?
For a simple validation tool with one parameter and a well-known standard, the description covers everything needed: what it validates, the exact format, its limitation. The presence of an output schema (not shown) is acceptable; the description does not need to explain return values.
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 single parameter 'value' has a generic schema description ('identifier or value to check'). The tool description adds significant meaning by specifying the expected format (8 or 11 characters with detailed segment breakdown), which helps the agent provide correct input.
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 clearly states it performs 'structural format validation only' for a SWIFT/BIC code and specifies the exact format (8 or 11 characters with breakdown). This distinguishes it from sibling validators (ean, iban, isbn, luhn, vateu) by naming the specific standard and limiting scope.
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 explicitly says 'only' structural validation and notes that country code is not cross-checked, guiding when not to use this tool. However, it does not explicitly name alternative tools for country validation, which would have made it a 5. Still clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vateu_validateAInspectFormat validation only (no VIES lookup) for an EU VAT number: checks the 2-letter member-state prefix (EL for Greece, XI for Northern Ireland) against that country's digit/letter pattern. A format-valid result does not mean the number is actually registered.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool only validates format, not real registration, explaining the logic (prefix check, pattern). It does not mention side effects, idempotency, or error behavior, but for a simple validation tool this is adequate.
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?
Two sentences, no redundant information. The key point 'Format validation only' is front-loaded, and every sentence adds value. Highly efficient.
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?
Given the tool's simplicity (1 required parameter, no annotations, output schema exists), the description is complete. It explains what the tool does, its limitations, and the format rules. The agent can correctly determine when to invoke it.
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 description coverage is 100% with a generic description 'identifier or value to check'. The description adds significant value by specifying the value is an EU VAT number, explaining the format rules (prefix, pattern), and clarifying limitations. This enriches the parameter semantics beyond the schema.
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 clearly states the tool does format validation of EU VAT numbers, specifying it checks the 2-letter prefix and digit/letter pattern. It distinguishes itself from siblings (EAN, IBAN, ISBN, etc.) by focusing on VAT numbers and explicitly mentioning 'no VIES lookup'.
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 provides explicit guidance: 'Format validation only (no VIES lookup)' and warns that a format-valid result does not mean registration. This helps the agent decide when to use this tool versus a registration lookup. It does not directly compare to sibling tools, but the different types are clear from names.
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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