commerce-validators

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

No annotations are provided, so the description carries full burden. It discloses the live DNS-over-HTTPS lookup, but does not mention potential side effects (rate limits, latency), or what happens on failure. An agent might benefit from knowing this is a network call.

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 the core function. Every sentence adds value: first states what and how, second provides rationale and use case. No wasted words.

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 no output schema, the description sufficiently explains the tool's purpose and usage. However, it lacks mention of return format or error handling. Still, it covers the essential behavioral context for an agent to decide to use 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 coverage is 0%, so description must compensate. It states the parameter is 'email_or_domain', which adds meaning beyond the schema, but does not specify expected format (e.g., full email vs. bare domain) or how domain is extracted from an email.

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 checks MX records via DNS-over-HTTPS to validate email deliverability. It distinguishes itself from sibling validation tools by focusing on email domains, with a specific use case of customer/supplier email 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?

The description explains when to use it ('before sending or invoicing') and why an LLM can't do it. It implies alternatives are other validation tools, but lacks explicit when-not-to-use instructions. The context from sibling tools partially covers this.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description fully carries the burden. It accurately describes the process: walking through gross sales, deductions, expected payout, and optionally flagging gaps with actual deposit. This provides good behavioral disclosure without contradictions.

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 is well-structured and front-loaded. Every word adds value, with no redundancy or wasted space.

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 tool with 6 parameters and no output schema, the description covers the key process and input relationships. However, it does not describe the output format (e.g., how the gap is flagged), which leaves some incompleteness. The step-by-step explanation is helpful.

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 0%, so the description must compensate. It references the key parameters (gross_sales, deductions, actual_deposit) in context but does not explain each parameter's syntax or role individually. This adds moderate value but falls short of fully compensating for missing schema documentation.

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's purpose: to explain why a payout is less than sales by walking through gross to deductions to expected, and flagging gaps. It uses specific verbs ('explain', 'walk', 'flag') and resources ('gross sales', 'deductions', 'actual_deposit'), and distinguishes from sibling validation tools.

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 usage when a payout is less than sales, but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 describes the calculation and conditional output, but does not disclose whether the tool has side effects (it likely does not), error conditions, or performance characteristics. The description provides basic behavior but lacks depth.

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 addressing the core formula and conditional behavior. No redundant or extraneous content. It is well-structured and front-loaded with the essential purpose.

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 4 parameters, 2 required, no output schema, and moderate complexity, the description explains the core calculation and optional reorder decision. However, it lacks details on the return format, edge cases (e.g., negative values, null on_hand behavior), and the structure of the output when on_hand is provided. The description is partially complete but leaves gaps for an agent to infer.

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 has 0% description coverage, so the description must add meaning. It explains the formula referencing avg_daily_sales, lead_time_days, safety_stock, and on_hand. It clarifies the role of safety_stock and the conditional logic with on_hand. However, it does not detail all parameters (e.g., default values, types beyond numeric) but significantly compensates for the missing schema descriptions.

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 defines the tool's purpose: calculating reorder point as lead-time demand plus safety stock. It also distinguishes behavior with or without 'on_hand' input. This is specific and distinct from sibling tools, which are all validation/check tools.

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 usage for inventory reorder decisions but provides no explicit guidance on when to use this tool vs alternatives. No exclusions or alternative references are given. Siblings are unrelated, so some implicit distinction exists, but lacking explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 describes the output and one parameter's behavior but does not disclose whether the tool has side effects, requires authentication, or its read-only nature. For a computation tool, the transparency is average.

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 concise, front-loaded with the main purpose, and every sentence adds value. No wasted words.

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 no output schema, the description lists return components adequately. However, parameter explanations are partial, leaving gaps for a 6-parameter tool. Completeness is adequate but not thorough.

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?

With 0% schema description coverage, the description must explain parameters. It details only 'fee_bearer' with enum values and hints at processing rates via defaults, but omits explanations for 'charge_amount', 'application_fee_pct', and 'application_fee_fixed'. Incomplete for 6 parameters.

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 the tool computes a Stripe Connect three-way split for one charge and lists the returns. It uses a specific verb-resource pair ('Compute split') and distinguishes itself from sibling validation tools.

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 the tool is for Stripe Connect fee calculations but does not specify when to use it versus alternatives or provide exclusions. No guidance on prerequisites or scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It explains the algorithm is a checksum and explicitly states 'nothing leaves the machine,' indicating no side effects, network calls, or destructive actions. This provides sufficient 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 two concise sentences, front-loading the purpose and immediately adding usage context and safety note. No extraneous words.

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 description covers purpose, usage, and behavior, but omits the return format (e.g., boolean, error message). Given simplicity, it is adequate but not fully complete for an agent without output schema.

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 coverage is 0%, so description must compensate. It specifies the parameter is a US ABA routing number with constraint '9 digits.' This adds meaning beyond the schema, though it could include a format pattern.

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 US ABA routing number by its checksum, specifying it is 9 digits. It distinguishes from sibling validation tools (validate_iban, validate_eu_vat, etc.) by focusing on US ABA numbers.

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 indicates when to use: 'catch typos before initiating an ACH/wire payout.' It implies the tool is appropriate for pre-payout validation. It does not explicitly exclude alternatives but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full weight. It discloses that it performs a 'live lookup against the official EU customs database,' indicating a network call and non-destructive behavior. It could mention potential latency or limits, but the current disclosure is sufficient for a validation tool.

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 three sentences, front-loaded with the core action, followed by context and example. Every sentence adds value with no redundant information.

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 (one parameter, no output schema), the description covers purpose, usage context, and input format completely. No gaps remain for an agent to select or invoke the tool correctly.

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 coverage is 0%, so the description must compensate. It provides a concrete example ('DE1234567890123') and explains the format (country code + number), adding essential meaning beyond the schema's bare string type and title.

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 verb 'Validate' and the resource 'EORI number', distinguishes it from sibling tools like validate_eu_vat and validate_iban by specifying the exact number type, and provides an example that makes the purpose unmistakable.

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 advises using the tool before customs filings or freight bookings, giving clear context. While it doesn't explicitly exclude alternative tools, the sibling names are distinct number types, so the usage context is implicitly well-defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. Discloses it performs live government lookup and returns validity + optional trader details. Lacks disclosure of rate limits, error handling, or service availability, but is mostly transparent given tool simplicity.

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?

Three sentences, each purposeful. Front-loaded with action and source, followed by usage context and input example. No unnecessary words or repetition.

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 single-parameter tool with no output schema or annotations, description covers purpose, input format, output content, and usage context. Minor gaps: no mention of error responses or handling of invalid inputs.

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 has 0% description coverage for vat_number parameter. Description fully compensates by providing input format examples ('DE811569869') and explanation ('country code + number'). Adds practical meaning 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?

Clearly states validation of EU VAT number against official VIES service. Specific verb (validate) and resource (EU VAT number) with no ambiguity. Distinguishes from sibling tools like validate_iban or validate_aba_routing by focusing on EU VAT.

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?

Explicitly advises use before invoicing/reverse-charging EU B2B customer. Notes that LLM cannot guess this information, implying necessity of live lookup. Could be more explicit about when not to use (e.g., non-EU VAT), but overall provides solid guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

States 'Pure-algorithm', indicating no side effects or external dependencies. No annotations provided, so description covers behavioral traits adequately. Could be slightly more explicit about lack of side effects, but sufficient.

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: first covers action and scope, second adds workflow context and nature. No redundant words, front-loaded. 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?

For a simple validation tool with one parameter and no output schema, description covers what it does, input format, and domain. Lacks explicit mention of return value (likely boolean), but highly usable. Near-complete for its complexity.

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 'code' has no schema description (0% coverage). The description compensates by specifying expected content: GTIN/UPC/EAN barcode with allowed lengths (8,12,13,14) and validation method (check digit). Adds meaningful format constraint.

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 verb 'validate' and resource 'GTIN/UPC/EAN barcode', specifying formats (GTIN-8/12/13/14) and function (check digit). The context 'in workflows' and sibling tools (all different validation types) make its purpose unambiguous.

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?

Implies use case: catching mistyped product barcodes in inventory/catalog workflows. No explicit comparison with sibling validation tools, but the domain-specific name and description suffice to distinguish from email, IBAN, VAT validators.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description bears the full burden. It discloses 'Pure-algorithm; no data leaves the machine', indicating it is a local computation with no side effects or network calls. This is a key behavioral trait for agent decision-making.

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 three sentences long, with no extraneous information. The purpose is stated first, followed by use-case and behavioral note. Every sentence adds value.

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 description lacks explicit mention of return value (e.g., boolean or validation details) despite no output schema. It implies validity checking but doesn't specify the output structure. For a validation tool, this is a noticeable gap.

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 coverage is 0% (the parameter 'iban' has no description in the schema). The description adds meaning by clarifying that the parameter is the IBAN to validate. Although it doesn't specify format constraints, the simplicity of the tool makes this sufficient.

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 'Validate an IBAN (International Bank Account Number) by structure + the ISO 7064 mod-97 checksum'. It specifies the resource (IBAN) and the action (validate), distinguishing it from sibling validation tools like validate_aba_routing or validate_eori.

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 advises using the tool 'before you initiate a transfer' to catch typos/invalid accounts. While it does not explicitly mention when not to use or alternatives, the context of IBAN validation is clear and the guidance is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations present, so description bears full burden. It discloses live fetching with 24h caching and optional date handling, but lacks details on response format, error behavior, or rate limits.

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 concise sentences front-load the main purpose and add essential details (source, caching, date, exceptions, examples) without waste.

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?

Covers purpose, parameters, and behavioral traits, but no output schema exists and description omits what the returned data format is (e.g., JSON structure). Error scenarios not addressed.

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 has 0% coverage; description compensates by explaining 'country_code' via examples and 'date' with format YYYY-MM-DD and purpose. Could explicitly state country_code is required and two-letter ISO.

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?

Description clearly states the tool retrieves EU VAT rates for a country, listing rate types, data source, and optional date filtering. It distinguishes from sibling validation tools like validate_eu_vat.

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 (for getting VAT rates by country), but no explicit when-to-use or when-not-to-use guidance or alternative tool recommendations are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.