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

Annotations already indicate read-only and idempotent behavior. The description adds value by explaining the two calculation modes (net vs gross input), the default currency, and the exact return field names. This goes beyond what annotations provide, giving the agent a clear understanding of behavior.

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 with two sentences. The first sentence captures the core functionality, and the second provides use cases. Every sentence is meaningful and front-loaded. No unnecessary 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 the tool has an output schema and 4 well-defined parameters, the description is complete. It explains the two input modes, the VAT rate interpretation, currency default, and typical usage scenarios. The output schema handles return value documentation.

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 has 100% description coverage, so the schema already documents all parameters well. The description provides context about the meaning of 'amount_type' and default currency, but this is already in the schema descriptions. Thus, the description adds minimal extra value 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 calculates VAT amounts from net or gross amounts for a given VAT rate. It provides the return object structure and examples of use cases. However, it does not explicitly differentiate from the sibling tool 'calculate_vat_breakdown', which might have a similar purpose.

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 when to use the tool: 'Use when building pricing tools, invoice calculators, or checkout flows that need to split gross prices into net + VAT components.' This provides clear context. It does not mention when not to use or alternatives, but the usage guidance is specific enough.

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?

Annotations already declare readOnlyHint and idempotentHint, so the tool's safety is clear. The description adds behavioral context by detailing the output structure (lines_summary, vat_breakdown, totals) and input requirements, which goes 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.

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

The description is two sentences, each informative and front-loaded. The first sentence explains the function and output, the second gives use cases. No unnecessary 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 the existence of an output schema, moderate complexity, and full parameter description coverage, the description is complete. It covers what the tool does, its inputs, outputs, and appropriate usage scenarios.

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 100%, so the description adds little new information about parameters beyond what the schema already provides. It restates the required fields for line items but does not enhance understanding of the 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?

Description starts with a specific verb 'Calculates' and resource 'VAT breakdown', and explicitly states the scope 'for a list of invoice line items'. It clearly distinguishes itself from sibling tools like calculate_vat_amount by focusing on a complete breakdown with grouping.

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 usage contexts: 'Use when generating invoices, building checkout summaries, or verifying VAT calculations'. It does not mention when not to use or alternatives, but the context is sufficiently 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?

Discloses exclusion of Saturdays, Sundays, and all 10 Portuguese national public holidays, plus the exact return shape. Annotations already indicate safe read (readOnlyHint, idempotentHint), so description adds valuable context without contradicting.

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 core function, and no wasted words. Every sentence adds value: first explains what it does, second gives usage guidance.

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 low complexity (2 params, no enums, output schema exists), the description fully covers purpose, usage, behavior, and return format with no gaps.

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?

Input schema already has 100% coverage with descriptions for both parameters (including format and example). Description does not add new semantic info beyond what schema provides, so baseline score of 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?

Description clearly states the tool counts working days between two dates, excluding weekends and all 10 Portuguese public holidays. It specifies the return shape and provides concrete use cases, distinguishing it from siblings like 'calculate_working_days_eu' which likely covers broader EU holidays.

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 gives three example use cases: Portuguese invoice payment deadlines, legal notice periods, and SLA response times. However, it does not explicitly mention when not to use this tool vs. the sibling 'calculate_working_days_eu', which would strengthen 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?

Beyond annotations (readOnlyHint, idempotentHint), the description details inclusive date range, holiday exclusion, and return value structure. No contradictions with annotations.

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 tight sentences: function, return values, then usage context and supported countries. Every sentence is essential 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?

With an output schema present, the description still covers return fields, parameter constraints, country list, and usage scenarios. It is fully complete for a simple 3-parameter 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?

Schema coverage is 100% and parameter descriptions are clear. The description adds minimal extra semantic value beyond schema (e.g., inclusive nature), but for a high-coverage schema, 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 counts working days between two dates for European countries, excluding weekends and national holidays. It distinguishes from siblings like 'calculate_working_days' by specifying country-specific holidays.

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 for cross-border SLA periods, invoice deadlines, and project timelines. While it doesn't list when not to use, the context is strong enough for an agent to infer appropriate 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?

Annotations already provide readOnly and idempotent hints. The description adds the output shape and the list of supported country codes, which are behavioral details beyond the schema and annotations.

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: purpose first, then return format and supported countries. No wasted words, front-loaded with key 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, the description covers purpose, supported locales, and return structure. It doesn't mention error handling for unsupported codes, but output schema exists. Nearly complete.

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 100%, but the description adds context about correct separators and a full list of supported country codes, which enhances parameter understanding beyond the schema definitions.

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 formats a number using European locale conventions, with specific verb and resource. It distinguishes well from sibling tools (VAT, holidays, 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 implies use for formatting numbers for European countries, and the context of sibling tools clarifies when to use it. However, no explicit when-not or alternatives are given.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is clear. The description adds the return structure but doesn't discuss permissions or rate limits. With annotations handling the core behavioral disclosure, a 3 is appropriate.

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 with no fluff: first states the function and return fields, second lists use cases. Front-loaded with purpose, every word earns its place.

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 one parameter, clear description, and an output schema (implied by the return field listing), the description is nearly complete. Minor gap: 'European country' in description vs. possibly non-EU, but that's a small detail.

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 100% with a clear description for 'country_code'. The tool description doesn't add additional parameter meaning beyond 'for a given European country', which is already implied by the schema. Baseline 3 is correct.

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 returns e-invoicing obligations for a given country, including specific fields like B2G/B2B/B2C mandatory flags, formats, and timeline. It distinguishes from sibling tools (VAT, working days, validation) by focusing on e-invoicing rules.

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 'Use when building invoice generation systems, determining compliance requirements for EU customers, or automating invoice submission workflows.' No explicit when-not-to-use or alternatives, but the context is clear and covers typical use cases.

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?

Annotations already indicate readOnly and idempotent. Description adds context about algorithm (Anonymous Gregorian) and the exact number of holidays, which is beyond annotations.

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 short sentences, each informative. 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?

Description mentions return value (11 holidays) and algorithm. With output schema present, it provides sufficient context for a tool with a single parameter and predictable output.

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 covers 100% of parameters with a clear description for the single parameter year. Description does not add extra meaning beyond the schema, so 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?

Description clearly states it returns all French national public holidays for a given year, specifying the number (11) and mentioning dynamic calculation for Easter-dependent holidays. This distinguishes it from sibling tools for other countries.

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 it is for French holidays in a given year. While no explicit when-not-to-use or alternatives are provided, the sibling tools for other countries implicitly guide selection.

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?

Annotations already declare read-only and idempotent. Description adds value by specifying the output structure ({country, mandatory_fields, optional_fields, notes}) and the data source (EU VAT Directive and local implementations). No 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?

Three sentences, each serving a distinct purpose: purpose, output shape, usage guidance. No fluff, front-loaded, and efficiently structured.

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 param, output schema exists), the description is complete enough. It mentions the return shape and usage context. Slight deduction because actual output schema details are not shown, but description covers key aspects.

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 100% with a well-described parameter (country_code with example values). Description does not add extra meaning beyond the schema, but given high coverage, a baseline of 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 returns mandatory fields for a valid VAT invoice for a European country, using specific verb 'Returns' and resource 'mandatory fields'. It distinguishes from siblings like 'validate_invoice_schema' and 'get_vat_rate' by focusing on invoice requirements.

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 says when to use: generating invoices, validating templates, compliance checks. Also includes a disclaimer about not being legal advice. Lacks explicit when-not-to-use or alternatives, but 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?

Annotations declare readOnlyHint=true and idempotentHint=true. The description adds details about skipping weekends and public holidays, applicable rules, and return structure, fully disclosing behavior without contradiction.

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: first states core purpose, second lists rules, third gives usage scenarios. No filler; front-loaded with essential 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?

The description covers purpose, parameters, rules, return structure (explicitly mentioning fields in returned object), and usage context. With good annotations and full schema, no gaps remain.

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 covers 100% of parameters with descriptions. The description adds examples (e.g., country codes, date format) and explains each rule's meaning, going beyond basic schema info.

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 calculates the next valid payment date based on a reference date and a rule for a European country. It lists all supported rules and return fields, distinguishing it from sibling tools (e.g., working days or 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 explicitly says 'Use when scheduling salary payments, invoice due dates, or any automated payment workflow that must avoid non-working days.' While it does not mention when not to use, the context is clear and no direct alternative exists among siblings.

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?

Annotations already indicate readOnly and idempotent. Description adds that the info is for reference only and not legal advice, which is valuable behavioral context beyond what annotations provide.

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?

Extremely concise: one sentence stating purpose, one listing output fields, one for use cases, and a disclaimer. Every sentence adds value with no 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?

Given a simple input schema and the existence of an output schema (implied by listed fields), the description is complete. It covers data source, output structure, use cases, and a necessary disclaimer.

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?

Input schema has 100% coverage with description for the single parameter. The description does not add additional meaning beyond what the schema already provides, so baseline score of 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 it returns legal B2B payment terms for European countries, specifying the exact data fields (default_days, max_days, etc.) and referencing EU Directive. It is distinct from sibling tools like get_vat_rate or validate_iban.

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 explicit use cases: generating invoices, setting payment due dates, automating AR workflows. Lacks explicit when-not-to-use or alternatives, but no direct sibling alternative exists.

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?

Annotations already indicate read-only and idempotent. The description adds that it returns exactly 10 mandatory holidays defined by law, with date, name, and English name fields. No 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?

Two sentences: first states purpose and output format, second lists use cases. No wasted words, 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 one-parameter tool with output schema and clear annotations, the description covers purpose, output structure, and usage context completely.

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 'year' is fully described in the schema (100% coverage). The description does not add extra parameter details beyond that, so 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 returns Portuguese national public holidays for a given year as a structured list, specifying the resource, action, and input. It distinguishes from sibling tools for other countries.

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 use cases like business deadlines, delivery dates, and SLA periods. It does not include when-not-to-use or alternative tools, 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?

Annotations already declare readOnlyHint and idempotentHint, indicating safety. The description adds value by detailing the output structure (e.g., holidays array with date/name) and listing supported countries, going 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.

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

The description is two sentences: the first defines purpose and output, the second gives use cases. It is front-loaded, concise, and every sentence is informative with no redundancy.

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 (3 parameters, output schema available, clear annotations), the description covers purpose, usage, and supported countries. It lacks details on error handling for unsupported countries or invalid dates, but overall is sufficient for an agent to invoke 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?

All three parameters are fully described in the input schema (100% coverage). The description adds extra context about supported country codes but does not significantly enhance parameter understanding 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 that the tool returns public holidays for a specified European country within a date range, with a list of supported countries. It differentiates from country-specific holiday tools (e.g., get_france_holidays) by being a range-based query, but does not explicitly contrast with sibling tools like calculate_working_days_eu.

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 when calculating SLA periods, project timelines, or delivery windows that skip non-working days, providing clear context. However, it does not explicitly state when not to use this tool versus alternatives like calculate_working_days_eu.

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?

Annotations already declare readOnlyHint and idempotentHint. The description adds behavioral details: 9 mandatory national holidays, output structure with date and names, and the exclusion of regional holidays. No contradictions with annotations.

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 pack all necessary information: what it does, output structure, and limitations. No redundant words, directly to the point.

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 annotations, full schema coverage, and an output schema, the description is complete enough. It clarifies the return format and the scope (national only), which is valuable for an agent comparing with sibling tools like get_france_holidays or get_public_holidays_range.

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 'year' is fully described in the schema (100% coverage). The description adds no new parameter meaning beyond the schema, but reinforces that the year is used to filter holidays, which is expected. Baseline 3 applies.

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 returns 'all Spanish national public holidays for a given year' as a structured list with specific fields. It distinguishes itself from sibling tools targeting other countries (e.g., get_france_holidays) and explicitly excludes regional holidays, providing precise 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 indicates when to use the tool (to obtain Spanish national public holidays) and when not (for regional holidays). It implicitly suggests alternatives like regional-specific tools, but does not explicitly name sibling tools for regional holidays.

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?

Annotations already declare readOnlyHint and idempotentHint. The description adds behavioral context by stating 'Information provided as reference only — not legal advice,' which sets expectations for the reliability of the output. No contradictions with annotations.

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 that pack the purpose, usage, output structure, and legal disclaimer without any wasted words. It is front-loaded with the primary function.

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, output schema exists), the description covers all necessary aspects: what it returns, when to use it, and a disclaimer. No gaps are evident for an AI agent to correctly invoke this 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?

Schema coverage is 100% with a single parameter described. The description adds marginal value by specifying 'European country' and providing example codes ('PT', 'DE', 'FR'), but the schema already covers the required format. The baseline of 3 is appropriate as the description does not significantly enhance parameter understanding.

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 verb 'returns', the resource (annual turnover threshold for VAT exemption), and the scope (European country). It also explicitly distinguishes this from sibling tools by naming the specific VAT scheme and mentioning the output structure.

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?

Description explicitly says when to use the tool: 'when determining if a small business needs to register for VAT, or when building onboarding flows for European freelancers and micro-enterprises.' It does not explicitly state when not to use, but the guidance is clear enough for an agent to infer appropriate contexts.

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?

Annotations already declare readOnlyHint and idempotentHint. Description adds that rates are numeric percentages, and error returns available codes. No contradictions; adds useful behavioral context beyond annotations.

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?

Description is a single paragraph but well-structured: returns, error case, supported countries, use cases. Slightly verbose but clear 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?

Covers return structure, error handling, supported countries, and use cases. Single required parameter, schema fully documented, output schema exists. Description is complete for the tool's purpose.

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?

Input schema covers the single parameter with examples. Description lists supported countries, adding value beyond schema. Baseline 3, plus for extra country list.

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 returns VAT rates for EU countries, listing specific rate types and a fallback error. The verb 'Returns' and resource 'VAT rates' are specific and distinct from sibling 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?

Explicitly lists when to use: 'Use when calculating EU cross-border invoice tax, determining correct rate for e-commerce checkout...' This provides clear context and differentiates from other tools.

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?

Annotations indicate readOnlyHint=true and idempotentHint=true, and the description aligns with a non-destructive suggestion tool. It adds context about the specific VAT scenarios covered and the returned structure, which complements the annotations without contradiction.

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 at about four sentences, front-loaded with the core purpose, followed by return structure, use cases, a specific rule (OSS for digital services), and a disclaimer. Every sentence adds value without redundancy.

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 complexity of EU VAT rules and the presence of a detailed output schema (implied by the description), the description covers purpose, inputs, outputs, and limitations. It is complete for an AI agent to decide to invoke the 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?

Schema coverage is 100% with detailed parameter descriptions. The description mentions the parameters (seller country, buyer country, etc.) but does not add significant new meaning beyond what the schema already provides, so the baseline score of 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 verb 'suggests', the resource 'VAT treatment', and the scope 'under EU VAT rules', covering specific scenarios like reverse charge, OSS/IOSS. It effectively distinguishes from siblings like 'calculate_vat_amount' which compute amounts rather than treatments.

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 use cases ('Use when building checkout VAT logic, invoice generation, or cross-border EU compliance workflows') and includes a disclaimer to verify with a tax advisor. However, it does not directly mention when not to use or suggest alternative tools, though the sibling context implies differentiation.

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?

Annotations already indicate readOnlyHint and idempotentHint, and the description adds that it applies the odd/even checksum algorithm and returns a boolean with optional reason. This provides useful context beyond the annotations without contradicting them.

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 paragraph of four sentences, all relevant and front-loaded with purpose. It is concise but could be slightly more structured (e.g., bullet points). However, it avoids 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?

Given the simple 1-parameter schema, high annotation quality, and description of return format, the description is complete. It covers the country, algorithm, valid use cases, and output structure. No gaps remain for the agent.

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 100%, with a clear description of the parameter (16-character code plus example). The tool description adds no further semantic information about the parameter beyond what the schema already provides, so a baseline score of 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 an Italian Codice Fiscale, specifies it is a 16-character alphanumeric code for individuals, and mentions the official checksum algorithm. It distinguishes itself from sibling validation tools (e.g., validate_nif, validate_partita_iva) by focusing on Italian personal fiscal codes.

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 lists use cases: processing Italian invoices, onboarding Italian individuals, or Italian compliance workflows. It does not explicitly state when not to use or mention alternatives, but the sibling context implies other tools for different countries/entities.

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?

Annotations already indicate read-only and idempotent behavior. The description adds crucial details: automatic space stripping, output format for valid and malformed inputs, and the limitation that it only validates structure/checksum. This exceeds what structured fields provide.

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 (under 200 words) and front-loaded with the core purpose. Every sentence adds necessary information—algorithm, countries, output, limitations—without redundancy.

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 simple parameter and presence of output schema (implied), the description fully covers validation algorithm, supported countries, input handling, return values, and limitations. No gaps remain for an agent to 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 100%, so baseline is 3. The description adds value by explaining that spaces are automatically stripped and providing an example IBAN. This clarifies the parameter format beyond the schema 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 that the tool validates an IBAN using the ISO 13616 MOD-97 algorithm and lists supported countries. It distinguishes itself from sibling validation tools (e.g., VAT, tax ID) by focusing specifically on IBAN 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 explicitly lists when to use the tool (SEPA transfers, direct debit mandates, e-commerce checkouts) and implicitly notes when not to use it by stating it does not confirm account existence. This differentiation guides correct selection among sibling 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds the return structure and a disclaimer about not being legal advice, which provides minor additional 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?

Description is brief, front-loaded with the purpose, includes return format and usage guidance. Every sentence is necessary without redundancy.

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, return format, usage context, and legal disclaimer. The output schema is described inline. It could optionally mention that required fields vary by country, but current content is sufficient for a validation tool with good annotations and 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 100%, so parameters are well-documented in the schema. The description does not add new semantics beyond mentioning 'mandatory fields', which is implied by the tool's purpose.

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 an invoice JSON for mandatory fields for VAT invoices in European countries, referencing EU Directive. It distinguishes from sibling validation tools (e.g., validate_vat_de, validate_nif) by focusing on invoice schema rather than individual identifiers.

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 recommends use in invoice generation pipelines, pre-submission validation, and compliance checks. While it doesn't list alternatives, the context makes the tool's role clear among siblings.

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?

Annotations declare readOnlyHint and idempotentHint, and the description adds behavioral details like 'applies the official weighted checksum algorithm' and return structure, which is consistent.

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 efficient sentences: function, method, and use cases. No redundant words, front-loaded with key 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?

With output schema and annotations, the description sufficiently covers return values and conditions. The use case guidance completes the context for an AI agent.

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 covers 100% of the parameter details, including data type and example. The description does not add additional semantics beyond what the schema provides, so baseline score of 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 it 'Validates a Dutch KVK chamber of commerce number' with specific format and checksum verification, distinguishing it from sibling tools like validate_vat_de or validate_codice_fiscale.

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 recommends use cases: 'processing Dutch invoices, validating Dutch suppliers, or onboarding Dutch business partners.' No when-not or alternatives mentioned, but the context is 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?

Beyond annotations (readOnlyHint, idempotentHint), adds that validation is offline, enforces first-digit rules, and returns specific failure reasons, but does not detail all possible failure scenarios.

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?

Well-structured with front-loaded purpose, algorithm, return format, use cases, and limitation; efficient but could be slightly shorter.

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?

Combined with schema and annotations, provides complete guidance for usage, return format, and limitations, requiring no additional context.

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 covers the one parameter with format and example; description adds checksum algorithm and first-digit rules, enriching meaning beyond 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?

Specifies validation of Portuguese NIF with modulo-11 checksum, clearly distinguishing from sibling tools like validate_nif_es.

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?

Lists concrete use cases (invoices, onboarding, compliance) and explicitly states it does not query the AT database, clarifying scope, though no explicit alternatives are mentioned.

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?

The description adds value beyond annotations: it explains the automatic detection of document type and the return format (valid, type, id). Annotations already indicate read-only and idempotent behavior, but the description provides necessary behavioral details.

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 with no wasted words. It efficiently conveys the tool's purpose, types, and return format.

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 has an output schema (mentioned in context) and the description covers the return structure. For a validation tool with one parameter, the description is complete and provides sufficient information for correct invocation.

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 100%, so the schema already documents the parameter. The description adds meaning by explaining the supported ID formats and examples, going beyond the base baseline of 3.

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 Spanish tax identification numbers and specifies three types (NIF, NIE, CIF) with brief format details. It distinguishes itself from sibling tools like 'validate_nif' by covering all three Spanish ID types.

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 clear context for when to use (for Spanish tax IDs) but does not explicitly state when not to use or mention alternatives. It implies use for Spanish IDs, but lacks exclusion 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?

Annotations already provide readOnlyHint=true and idempotentHint=true, indicating safety. The description adds value by detailing the return format ({ valid: boolean, partita_iva: string } or { valid: false, reason: string }) and the checksum algorithm used. No contradictions with annotations.

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 purpose, and contains no redundant information. Every sentence contributes meaning (purpose, algorithm, return types, use cases). Ideal length for this simple tool.

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 one parameter with full schema coverage and an output schema (indicated by context signals), the description covers input format, algorithm, return shape, and use cases. It is fully sufficient for an agent to understand and 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?

The input schema has 100% coverage, describing the parameter with an example and allowing spaces. The description does not significantly add new meaning beyond the schema; it reiterates the 11-digit nature but does not provide additional constraints or format details not already in 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 validates an Italian Partita IVA (VAT number), specifies it is an 11-digit number, and mentions the official Luhn-variant checksum algorithm. It effectively distinguishes itself from sibling tools that validate other country-specific identifiers (e.g., validate_vat_de, validate_tva_fr).

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 states when to use the tool: 'processing Italian B2B invoices, validating Italian suppliers, or any Italian business compliance workflow.' While it does not explicitly mention when not to use it or list alternatives, the context from sibling tools (all country-specific) makes the usage 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?

Annotations already provide readOnlyHint and idempotentHint, indicating safe and idempotent operation. The description adds value by specifying the return structure (valid, postal_code, country, format) and listing supported country formats, which goes beyond what annotations provide. No 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 concise and packs necessary information (supported countries, formats, use cases) into a single paragraph. It could be slightly better structured (e.g., bullet points for countries) but is efficient and front-loaded with 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 the complexity of multiple country patterns, the description covers purpose, parameters, output structure, and use cases thoroughly. The output schema is mentioned, and annotations handle safety. No gaps remain for an agent to select and invoke this 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 100%, but the description adds significant context: example postal codes for various countries, clarification that country_code is two-letter ISO, and details of the return format. This enhances understanding beyond the schema alone.

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 postal code format for European countries using official patterns, lists supported countries and formats, and mentions use cases (e-commerce, address verification, logistics). It distinguishes itself from sibling validation tools like validate_iban or validate_nif by focusing specifically on postal codes.

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 through listed supported countries and example use cases, but does not explicitly state when to use versus alternatives or when not to use. However, given the specificity of postal code validation, the guidance is clear enough for an agent.

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?

Annotations already indicate readOnly and idempotent. The description adds behavioral context beyond annotations: mentions the Luhn algorithm, return fields, and automatic handling of the La Poste special case. No 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?

Three focused sentences with no fluff. Front-loaded with the main purpose, then details on structure, return, and special case handling. 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?

Given the tool's simplicity (single parameter) and the presence of an output schema, the description covers all necessary information: purpose, input format, output fields, and a special case. No gaps.

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 'siret' has 100% schema coverage, but the description adds meaning beyond the schema by explaining the SIRET structure (SIREN + establishment) and providing an example with spaces/dashes.

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 French SIRET using the Luhn algorithm, explains its structure (SIREN + establishment), and lists the return fields. It is specific and distinct from sibling validation tools for other identifiers.

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 indicates when to use this tool (when a SIRET number needs validation), but does not explicitly compare to alternatives or provide when-not usage. The sibling tools are for different identifier types, so 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?

Annotations already indicate readOnlyHint and idempotentHint. The description adds transparency by detailing the validation formula for numeric keys and the return structure, going beyond what annotations provide without contradiction.

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 that are perfectly concise: the first defines the function and format, the second covers validation logic and return shape. 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 output schema is described in the description, annotations cover safety, and no nested objects, the description is complete for an agent to understand invocation and outcome.

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 description adds value by providing an example and explaining the format requirement, reinforcing what the schema states.

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 a French TVA intracom number, specifies the required format 'FR' + 2 alphanumeric key + 9-digit SIREN, and lists return fields. It distinctly sets itself apart from sibling tools that validate VAT numbers for other countries.

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 French VAT validation without explicit when-to-use or when-not-to-use guidance. However, given sibling tools are other country VAT validators, the context is clear enough for an agent to select this tool for French numbers.

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?

Annotations already indicate readOnly and idempotent. Description adds the validation algorithm (ISO 7064 MOD-11-10) and return shape. No 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?

Concise, well-structured, front-loaded with purpose and format. Each sentence adds value without redundancy.

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?

Fully covers the tool's behavior, input, and output. No missing aspects for a single-parameter 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?

Schema coverage is 100%, but description adds semantic details: format explanation, allowance of spaces, and link to official algorithm. Enhances understanding beyond 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 it validates a German VAT number, specifies the format 'DE' followed by 9 digits, and mentions the checksum algorithm. It differentiates from sibling validation tools by being country-specific.

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 provides use cases: processing German invoices, validating German suppliers for intra-EU transactions, and B2B workflows involving German companies. No exclusion mentioned, but clear context given.

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?

Disclosures beyond annotations: modulo-97 algorithm, return shape, and format variants. No 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?

Two concise sentences with clear structure: function, algorithm, usage. 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?

Complete for a simple validation tool: covers format, algorithm, usage, and output. No missing essential details.

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 covers parameter well (100% coverage). Description adds value by detailing format variations and spaces allowed, improving clarity.

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 it validates UK VAT numbers with specific format and algorithm, distinguishing it from sibling tools for other countries.

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 explicit use cases (UK invoices, post-Brexit, B2B workflows), but lacks when-not or alternative tools.

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