Latin American data validation tools for AI agents. Validates Brazilian CPF, CNPJ and PIX keys, Mexican RFC, Chilean RUT, and provides public holidays for Brazil, Mexico and Chile.
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Average 4.5/5 across 11 of 11 tools scored.
Each tool targets a distinct resource (a specific country's holidays or a specific identifier type). The four holiday tools are clearly separated by country, and the seven validation tools are each for a different document or identifier (CNPJ, CPF, CUIL, CUIT, PIX key, RFC, RUT). No two tools appear to overlap in purpose.
All tool names follow a consistent pattern: get_<country>_holidays for holiday lookups and validate_<identifier> for validation. Naming is entirely lowercase snake_case with a clear verb prefix, making it easy to predict tool names.
With 11 tools, the server is well-scoped for its purpose of providing Latin American business utilities. The set is neither too sparse nor overly large, and each tool contributes a distinct function. This count is appropriate for the intended domain.
The tool surface covers the major Latin American economies (Argentina, Brazil, Chile, Mexico) for both holidays and key identifiers. Minor gaps exist, such as missing validation for Peruvian RUC or Colombian NIT, and holiday coverage is limited to four countries. However, the core workflows for the included countries are well covered.
get_argentina_holidaysAReturns Argentine national public holidays for any given year. Use this tool when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Argentina. Returns all national holidays with dates in YYYY-MM-DD format and names in both Spanish and English. Note: Argentina also has bridge holidays (feriados puente) declared annually by the government which are not included here.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | The year to get holidays for. Example: 2026 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description does not contradict them. The description adds behavioral context by specifying the return format (dates in YYYY-MM-DD), the bilingual names, and that bridge holidays (feriados puente) are not included, which is a meaningful limitation beyond what annotations provide. It does not cover error handling or edge cases, but the added detail justifies a score above baseline.
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 compact, informative, and well-structured. It front-loads the core purpose, then gives usage guidance, return format, and a caveat—all in four sentences with no redundant phrasing. Every sentence contributes value, making it an example of efficient communication.
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, read-only, no output schema), the description is complete. It explains what is returned (national holidays), the format (dates and names), the language coverage, and the notable omission (bridge holidays). This is sufficient for an agent to understand the tool's behavior without needing an 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?
The input schema already describes the 'year' parameter with a 100% coverage, including an example ('2026'). The description adds minimal semantic detail beyond saying 'any given year,' which is already implied. Since schema coverage is high, the baseline of 3 is appropriate, and the description does not meaningfully enhance parameter comprehension.
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 Argentine national public holidays for any given year,' which specifies the exact verb (Returns), resource (Argentine national public holidays), and scope (any given year). It also distinguishes from sibling tools like get_brazil_holidays by focusing on Argentina. The additional details about date format and bilingual names further clarify the 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 'Use this tool when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Argentina,' providing clear usage contexts. However, it does not explicitly name alternative tools for other countries or state a 'when not to use' scenario beyond the note that bridge holidays are excluded, which implies a limitation but not an alternative. This is clear context without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_brazil_holidaysAReturns Brazilian national public holidays for any given year. Use this tool when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Brazil. Returns all national holidays with dates in YYYY-MM-DD format and names in both Portuguese and English. Note: Brazil also has state and municipal holidays which vary by location and are not included here.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | The year to get holidays for. Example: 2026 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by disclosing the return format (YYYY-MM-DD), bilingual names (Portuguese and English), and the exclusion of state/municipal holidays, which goes beyond the annotations and provides useful 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 three sentences, front-loaded with the core purpose, followed by usage scenarios and return details. Every sentence contributes meaningful information 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?
No output schema exists, but the description clearly explains what is returned (dates in YYYY-MM-DD and names in Portuguese/English) and notes limitations (state/municipal holidays excluded). This makes it complete for the tool's simple scope.
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% for the single 'year' parameter with description and example provided. The description does not add much beyond mentioning 'any given year', so baseline 3 is appropriate; no need for further compensation.
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 starts with 'Returns Brazilian national public holidays for any given year', which is a specific verb+resource combination. It clearly distinguishes from sibling tools for other countries by specifying 'Brazilian' and further clarifying it covers only national, not state or municipal, 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 states when to use: 'Use this tool when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Brazil.' It also notes state and municipal holidays are not included, which guides against alternative tools that might cover those.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_chile_holidaysAReturns Chilean national public holidays for any given year. Use this tool when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Chile. Returns all national holidays with dates in YYYY-MM-DD format and names in both Spanish and English.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | The year to get holidays for. Example: 2026 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. The description adds behavioral context by specifying the date format (YYYY-MM-DD) and bilingual names (Spanish and English), which goes beyond the annotations. 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 two sentences: the first states what the tool does, the second states when to use it and what it returns. Every sentence earns its place 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?
For a simple read-only holiday lookup with one parameter and no output schema, the description adequately covers usage, return format, language, and scope. It does not mention error handling or edge cases, but the tool's simplicity makes this acceptable.
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%: the schema already describes the 'year' parameter with an example. The description's phrase 'any given year' adds no new information, so the baseline of 3 is appropriate since the schema does the heavy lifting.
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 Chilean national public holidays for any given year. The verb 'Returns' plus the specific resource (Chilean holidays) distinguishes it from sibling tools for other countries. It also specifies output format and languages.
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 gives explicit use cases: calculating delivery dates, scheduling appointments, computing working days. It implies Chile-specific usage, though it does not explicitly name sibling alternatives like get_argentina_holidays for other countries, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_mexico_holidaysAReturns Mexican national public holidays for any given year. Use this tool when calculating delivery dates, scheduling appointments, or any task requiring knowledge of non-working days in Mexico. Returns all national holidays with dates in YYYY-MM-DD format and names in both Spanish and English. Note: some Mexican holidays fall on the nearest Monday (puente) — the dates returned are the fixed calendar dates as established by law.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | The year to get holidays for. Example: 2026 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, covering safety. The description adds valuable context about the 'puente' (nearest Monday) rule, clarifying that the dates returned are fixed legal calendar dates, not actual observance dates. This non-obvious behavior is critical to prevent misinterpretation and 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 three sentences with no wasteful words: purpose, usage scenarios, output format, and a caveat. It is well-structured, front-loaded with the primary action, and every sentence 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?
For a simple read-only tool with one parameter, the description covers purpose, usage, output format (dates in YYYY-MM-DD, names in Spanish and English), and a behavioral nuance. Since there is no output schema, a slightly more explicit return structure (e.g., array of objects) could elevate completeness, but it remains sufficiently informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully describes the single 'year' parameter with type and example, so the description adds minimal extra meaning. It merely mentions 'any given year' and the output format, which aligns with the baseline score of 3 for high schema coverage.
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 begins with a clear action verb ('Returns') and specifies the resource as 'Mexican national public holidays' for any given year. This thoroughly distinguishes it from sibling tools for other countries (Argentina, Brazil, Chile) and validation tools, leaving no ambiguity about what the tool does.
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: for calculating delivery dates, scheduling appointments, or any task requiring knowledge of non-working days in Mexico. It does not explicitly mention when not to use it or alternatives, but the sibling context and the country-specific scope make the boundaries clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_cnpjAValidates a Brazilian CNPJ (Cadastro Nacional da Pessoa Jurídica) using the official Receita Federal checksum algorithm. Use this tool when processing Brazilian company registrations, B2B invoices, supplier onboarding, e-commerce orders, or any document requiring a valid Brazilian company taxpayer number. Input must be a 14-digit string (with or without formatting). Returns whether the CNPJ is mathematically valid, along with the cleaned CNPJ. Does not verify if the CNPJ is active in the Receita Federal database.
| Name | Required | Description | Default |
|---|---|---|---|
| cnpj | Yes | The Brazilian CNPJ to validate. Formatting (dots, slash and dash) is automatically removed. Example: '11.222.333/0001-81' or '11222333000181' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and idempotentHint annotations, the description discloses the algorithm used, input format flexibility, the return value (mathematical validity and cleaned CNPJ), and a key limitation (no active status verification). This is rich behavioral context with no contradiction to 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 three concise sentences, front-loaded with the main action, then usage context, then input/output specifics. Every sentence earns its place with 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?
For a single-parameter tool with detailed annotations and no output schema, the description is complete. It covers what the tool does, when to use it, input requirements, return value, and a key limitation, making it fully actionable for an 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?
The input schema already provides 100% coverage with a description of the 'cnpj' parameter. The description adds the specific constraint 'Input must be a 14-digit string,' which is not explicitly stated in the schema. This adds meaningful clarification beyond the schema's example.
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 Brazilian CNPJ' using the official Receita Federal checksum algorithm, with a specific resource and scope. It distinguishes from sibling validation tools (e.g., validate_cpf, validate_cuit) by naming the CNPJ specifically and mentioning it returns validity and the cleaned CNPJ.
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 states when to use the tool: 'Use this tool when processing Brazilian company registrations, B2B invoices, supplier onboarding, e-commerce orders, or any document requiring a valid Brazilian company taxpayer number.' It also provides an exclusion: 'Does not verify if the CNPJ is active in the Receita Federal database,' guiding users away from using it for that purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_cpfAValidates a Brazilian CPF (Cadastro de Pessoas Físicas) using the official Receita Federal checksum algorithm. Use this tool when processing Brazilian user registrations, invoices, tax forms, e-commerce orders, or any document requiring a valid Brazilian individual taxpayer number. Input must be an 11-digit string (with or without formatting). Returns whether the CPF is mathematically valid, along with the cleaned CPF. Does not verify if the CPF exists in the Receita Federal database — only validates the format and checksum.
| Name | Required | Description | Default |
|---|---|---|---|
| cpf | Yes | The Brazilian CPF to validate. Formatting (dots and dash) is automatically removed. Example: '123.456.789-09' or '12345678909' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=true, idempotentHint=true), the description discloses the verification algorithm, the return behavior ('Returns whether the CPF is mathematically valid, along with the cleaned CPF'), and the key limitation that it only validates format/checksum, not database existence. This provides full transparency for a read-only 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 concise, front-loaded sentences. The first sentence states the primary purpose, the second gives usage context, and the third details input/output and limitations. Every sentence contributes unique information, with no redundancy or fluff.
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 parameter, no output schema, strong annotations), the description covers all essential aspects: purpose, usage scenarios, input format, return value, and limitations. It is fully self-contained and sufficient for an agent to select and invoke the tool correctly, especially compared to similar sibling validation tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the 'cpf' parameter well with an example and notes formatting is automatically removed (100% coverage). The description adds the 11-digit length constraint and explicitly allows 'with or without formatting', which reinforces schema semantics without conflicting. Minor added value, hence 4 rather than 5.
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 'Validates' with the specific resource 'Brazilian CPF' and the method 'official Receita Federal checksum algorithm', distinguishing it from sibling validation tools like validate_cnpj and validate_cuil. It also specifies the exact use cases (Brazilian user registrations, invoices, tax forms), leaving no ambiguity about the tool's 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 states when to use the tool: 'processing Brazilian user registrations, invoices, tax forms, e-commerce orders, or any document requiring a valid Brazilian individual taxpayer number'. It also provides a critical exclusion: 'Does not verify if the CPF exists in the Receita Federal database', which tells users when not to use it and implies an alternative for existence checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_cuilAValidates an Argentine CUIL (Código Único de Identificación Laboral) using the official ANSES checksum algorithm. CUIL is the labor identification number assigned to all workers and employees in Argentina. Use this tool when processing Argentine payroll, employment contracts, social security forms, HR onboarding, or any document requiring a valid Argentine labor identifier. The validation algorithm is identical to CUIT. Returns whether the CUIL is valid and the cleaned CUIL.
| Name | Required | Description | Default |
|---|---|---|---|
| cuil | Yes | The Argentine CUIL to validate. Formatting (dashes) is automatically removed. Example: '20-12345678-9' or '20123456789' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idempotentHint, and the description adds that dashes are automatically removed, the algorithm is official, and the return value includes validity and cleaned CUIL. This gives useful behavioral context 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 moderately sized with the main action front-loaded, followed by context and usage. Each sentence adds value, though it could be tightened slightly.
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, the description covers purpose, use cases, algorithm, and return value, making it fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the single 'cuil' parameter fully, and the description adds practical meaning by mentioning formatting removal and providing an example, which goes 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 validates Argentine CUIL using the ANSES checksum algorithm and explains what a CUIL is. It distinguishes from siblings by specifying the Argentine labor identifier, making it 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?
It explicitly says when to use it: for Argentine payroll, employment contracts, social security forms, HR onboarding, or any document requiring a valid Argentine labor identifier. It notes the algorithm is identical to CUIT, but does not explicitly name an alternative or exclude CUIT validation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_cuitAValidates an Argentine CUIT (Código Único de Identificación Tributaria) using the official AFIP checksum algorithm. CUIT is used by companies, self-employed workers, and other entities for tax purposes. Use this tool when processing Argentine invoices, supplier registrations, B2B transactions, or any document requiring a valid Argentine tax identifier. Accepts CUIT with or without formatting (dashes). Returns whether the CUIT is valid, the entity type detected, and the cleaned CUIT.
| Name | Required | Description | Default |
|---|---|---|---|
| cuit | Yes | The Argentine CUIT to validate. Formatting (dashes) is automatically removed. Example: '30-12345678-9' or '30123456789' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description discloses that the tool uses the official AFIP checksum algorithm, accepts both formatted and unformatted CUITs, automatically removes dashes, and returns validity, entity type, and cleaned CUIT. This gives the agent a full behavioral picture.
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, front-loaded with the purpose, then usage guidance, then behavior and return values. Every sentence earns its place with no redundancy or filler.
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 validation tool with readOnly and idempotent annotations, the description is complete: it covers purpose, usage triggers, input flexibility, algorithm, and output fields. No output schema exists, but the description explicitly states what is returned, filling that 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?
The schema already covers 100% of the parameter with a description and example. The tool description adds value by clarifying that both formatted and unformatted CUITs are acceptable and that the tool returns a cleaned version, reinforcing parameter 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?
The description opens with 'Validates an Argentine CUIT' using a specific verb and resource, clearly identifying what the tool does. It also distinguishes from sibling validation tools by specifying Argentina's CUIT and the official AFIP checksum algorithm.
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: 'Use this tool when processing Argentine invoices, supplier registrations, B2B transactions, or any document requiring a valid Argentine tax identifier.' It provides clear context but does not mention exclusions or alternatives, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_pix_keyAValidates a Brazilian PIX key format. PIX is Brazil's instant payment system. Use this tool when processing Brazilian payments, validating payment forms, or any fintech application handling Brazilian transfers. Supports all PIX key types: CPF (11 digits), CNPJ (14 digits), email, Brazilian phone number (+55 format), and EVP (random key UUID format). Returns whether the key is valid and the detected key type.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | The PIX key to validate. Can be a CPF, CNPJ, email, phone number (+5511999999999) or EVP UUID. Example: 'user@email.com' or '+5511999999999' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds the return behavior—'Returns whether the key is valid and the detected key type'—and enumerates supported formats, which enriches understanding beyond the structured 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, front-loaded with the core purpose, followed by usage context and supported formats. It is concise, with no redundant information, and every sentence contributes to understanding the 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?
For a simple validation tool with one parameter, no output schema, and clear annotations, the description is complete. It covers purpose, usage context, supported input formats, and return value, making it fully 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?
Schema coverage is 100%, with the key parameter described in detail including examples. The description adds a list of accepted formats (CPF, CNPJ, email, phone, EVP) and explains the phone format (+55), complementing the schema but not carrying the full burden since the schema already documents the parameter well.
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 'Validates a Brazilian PIX key format' with a specific verb and resource. It distinguishes itself from sibling validators by focusing on PIX keys, even listing all supported key types, making the tool's 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?
The description explicitly states 'Use this tool when processing Brazilian payments, validating payment forms, or any fintech application handling Brazilian transfers,' providing clear usage context. However, it does not explicitly mention alternatives for standalone CPF/CNPJ validation, though the sibling list implies such tools exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_rfc_mxAValidates a Mexican RFC (Registro Federal de Contribuyentes) format for both individuals (13 characters) and companies (12 characters). Use this tool when processing Mexican invoices (CFDI), tax forms, supplier registrations, or any document requiring a valid Mexican taxpayer identifier. Returns whether the RFC format is valid, the detected type (individual or company), and the cleaned RFC. Note: validates format only, does not verify against the SAT registry.
| Name | Required | Description | Default |
|---|---|---|---|
| rfc | Yes | The Mexican RFC to validate. Spaces are automatically removed. Example: 'GODE561231GR8' for individual or 'GME9412171A3' for company |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and idempotent behavior, and the description adds crucial context: it validates format only and does not verify against the SAT registry. This warns the agent about a potential false sense of completeness. Also specifies the exact return values (validity, type, cleaned RFC), going well 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?
The description is three sentences, starts with the core action, provides practical use cases, and closes with an important limitation. Every sentence earns its place with no filler, making it efficient and well-organized.
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 single-parameter tool with no output schema, the description fully covers what the tool does, when to use it, what it returns, and its limitation. It is complete and self-contained for this straightforward validator.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description for 'rfc' already includes an example and notes that spaces are automatically removed, giving high coverage. The tool description further adds meaning by explaining the 13-character individual and 12-character company formats, which helps the agent understand validation expectations and form valid inputs.
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 Mexican RFC format for both individuals and companies, with specific character counts. It distinguishes from siblings by explicitly focusing on Mexican taxpayer identifiers and mentions return values, making the 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?
The description provides explicit use cases such as processing Mexican invoices (CFDI), tax forms, and supplier registrations. While it does not explicitly list when-not-to-use or name alternatives, the sibling context and 'Mexican RFC' phrasing make the domain clear. Lacks explicit exclusions but still offers strong guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_rut_clAValidates a Chilean RUT (Rol Único Tributario) using the official Chilean modulo-11 checksum algorithm. Use this tool when processing Chilean invoices, tax forms, user registrations, e-commerce orders, or any document requiring a valid Chilean taxpayer identifier. Accepts RUT with or without formatting (dots and dash). Returns whether the RUT is valid and the cleaned RUT. Does not verify if the RUT is active in the SII registry.
| Name | Required | Description | Default |
|---|---|---|---|
| rut | Yes | The Chilean RUT to validate. Formatting (dots and dash) is automatically removed. Example: '12.345.678-9' or '123456789' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, but the description adds valuable context: the modulo-11 algorithm, automatic formatting removal, return value (valid status and cleaned RUT), and the explicit limitation that it does not verify SII registry activity. These go beyond annotation hints and inform the agent of exact 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 front-loaded with the primary purpose, followed by usage guidance and behavioral notes. Each sentence serves a distinct function, and the overall length (about 70 words) is appropriate for a simple validation tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple: one parameter, no output schema. The description covers the return value and a key limitation (SII registry), which is sufficient for an agent to select and invoke the tool correctly. No critical information is missing.
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 'rut' is fully described in the schema (100% coverage), including formatting removal and an example. The description repeats the formatting acceptance but adds no new semantic information about the parameter itself. Baseline 3 is appropriate as the schema already handles the heavy lifting.
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 Chilean RUT' using the 'official Chilean modulo-11 checksum algorithm.' The verb is specific, the resource is clear, and the algorithm details distinguish it from sibling validators 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 gives explicit use cases: 'when processing Chilean invoices, tax forms, user registrations, e-commerce orders, or any document requiring a valid Chilean taxpayer identifier.' However, it does not explicitly name alternative tools or when NOT to use it, though it does mention a limitation about SII registry verification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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