MCP LatAm Tools

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a read-only operation (implied by 'Returns'), specifies the output format ('dates in YYYY-MM-DD format and names in Portuguese and English'), and clarifies limitations (excludes state/municipal holidays). However, it doesn't mention potential errors (e.g., invalid year inputs) or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage guidelines and output details, with no wasted words. Every sentence adds value, such as clarifying exclusions and formats.

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 low complexity (1 parameter, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, output format, and limitations. However, it lacks details on error handling or response structure, which could be useful for an agent. No output schema exists, so some explanation of return values is provided.

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%, so the schema already documents the single parameter 'year' with an example. The description adds no additional parameter details beyond what the schema provides, such as valid year ranges or handling of edge cases. Baseline 3 is appropriate when 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 specific action ('Returns Brazilian national public holidays') and resource ('for any given year'), distinguishing it from sibling tools like get_chile_holidays and get_mexico_holidays by specifying Brazil. It goes beyond the name/title by explaining scope and format.

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 this tool ('when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Brazil') and provides clear exclusions ('Brazil also has state and municipal holidays not included here'), guiding users away from misuse for local 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?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it's a read-only operation (implied by 'Returns'), specifies the return format ('dates in YYYY-MM-DD format and names in Spanish and English'), and mentions the scope ('national public holidays'). However, it doesn't cover potential limitations like rate limits or error conditions.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage guidelines and return format details. Every sentence adds value without redundancy, making it efficient and well-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 low complexity (1 parameter, no annotations, no output schema), the description is largely complete: it explains purpose, usage, and return format. However, without an output schema, it could benefit from more detail on the exact structure of returned data (e.g., array of objects with specific fields).

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 coverage is 100%, so the schema already fully documents the single 'year' parameter. The description doesn't add any additional meaning about parameters beyond what's in the schema (e.g., format constraints or special cases), meeting the baseline 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 clearly states the specific action ('Returns Chilean national public holidays') and resource ('for any given year'), distinguishing it from sibling tools like get_brazil_holidays and get_mexico_holidays by specifying the country. It provides a complete picture of 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 this tool ('when calculating delivery dates, scheduling appointments, or any task requiring knowledge of non-working days in Chile'), providing clear context and distinguishing it from validation tools like validate_rut_cl. It effectively guides the agent on appropriate 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: returns all mandatory national holidays, with dates in YYYY-MM-DD format and names in Spanish and English. However, it doesn't mention potential limitations like data source, update frequency, or regional variations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences, front-loaded with the core purpose, followed by usage guidance and output details. Every sentence adds value without redundancy, making it efficient and well-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?

For a simple read-only tool with one parameter and no output schema, the description is largely complete: it covers purpose, usage, and output format. However, without annotations or output schema, it could benefit from mentioning return structure (e.g., array of objects) or error handling.

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%, so the schema already documents the single 'year' parameter. The description adds no additional parameter semantics beyond what's in the schema, but doesn't contradict it either, meeting the baseline 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 clearly states the specific action ('Returns Mexican national public holidays') and resource ('for any given year'), distinguishing it from sibling tools like get_brazil_holidays and get_chile_holidays by specifying the geographic scope (Mexico).

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 this tool: 'when calculating delivery dates, scheduling appointments, or any task requiring knowledge of non-working days in Mexico.' This provides clear context and distinguishes it from validation tools in the sibling list.

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

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

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it validates using an official algorithm, accepts both formatted and unformatted input, and returns validation status plus formatted CNPJ. It doesn't mention error handling or performance limits, but covers the core functionality adequately.

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 that are front-loaded with purpose, followed by usage guidelines and input/output details. Every sentence adds value with zero waste, making it efficient and well-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?

For a single-parameter validation tool with no annotations and no output schema, the description is quite complete: it explains what it does, when to use it, input flexibility, and return values. It could slightly improve by specifying error cases or output format details, but it's largely sufficient.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter 'cnpj' with examples and format details. The description adds marginal value by reiterating acceptance of formatted/unformatted input, but doesn't provide additional syntax or constraints 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 specific action ('validates'), resource ('Brazilian CNPJ'), and method ('using the official Receita Federal checksum algorithm'). It distinguishes from sibling tools like validate_cpf, validate_rfc_mx, etc., by specifying the Brazilian company taxpayer number context.

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 this tool: 'when processing Brazilian company registrations, B2B invoices, supplier onboarding, or any document requiring a valid Brazilian company taxpayer number.' It provides clear context for usage without needing to mention alternatives, as the sibling tools are for different countries/document types.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it validates using a checksum algorithm, accepts formatted or unformatted input, returns validity status and cleaned CPF, and clarifies it doesn't check database existence. However, it doesn't mention error handling, rate limits, or authentication needs, leaving some behavioral aspects uncovered.

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 core purpose, followed by usage guidelines and behavioral details. Every sentence adds essential information: validation method, use cases, input format, return values, and limitations. There is no wasted text, and the structure flows logically from general to specific.

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 moderate complexity (single parameter, no output schema, no annotations), the description is largely complete. It covers purpose, usage, input semantics, and behavioral traits. However, without an output schema, it could more explicitly detail the return structure (e.g., format of 'cleaned CPF'), though it does mention what is returned.

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%, so the baseline is 3. The description adds value by explaining the input format ('11 digits with or without formatting like 123.456.789-09'), which complements the schema's example. It doesn't provide additional syntax details beyond the schema, but the context about digit count and formatting options enhances 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?

The description clearly states the specific action ('validates a Brazilian CPF'), the resource ('Cadastro de Pessoas Físicas'), and the method ('using the official Receita Federal checksum algorithm'). It distinguishes this tool from sibling tools like validate_cnpj, validate_rfc_mx, etc., by specifying it's for Brazilian individual taxpayer numbers rather than other national 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 explicitly lists multiple scenarios when to use this tool: 'processing Brazilian user registrations, invoices, tax forms, e-commerce orders, or any document requiring a valid Brazilian individual taxpayer number.' It also clarifies when not to use it: 'Does not verify if the CPF exists in Receita Federal database,' which helps avoid misuse for existence checks.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it validates format (not authenticity), supports 4 key types with examples, and returns key type and validity. However, it lacks details on error handling, performance, or limitations (e.g., length constraints, international phone formats beyond +55), leaving some gaps in 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 front-loaded with the core purpose, followed by context (PIX system), usage guidelines, supported types, and return values—all in three efficient sentences with zero waste. Each sentence adds critical information, making it highly structured and concise.

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 low complexity (1 parameter, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, key types, and returns. However, without annotations or output schema, it could benefit from more behavioral details (e.g., error cases, validation rules per type) to fully compensate, leaving a minor gap.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by listing all 4 PIX key types (CPF/CNPJ, email, phone, EVP) and specifying the phone format (+55), which enriches the schema's single example. However, it doesn't provide additional syntax rules or validation nuances beyond what's implied in the schema, keeping it slightly above baseline.

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 specific action ('validates a Brazilian PIX key format') and resource ('PIX key'), distinguishing it from sibling tools like validate_cnpj or validate_cpf by focusing on the broader PIX system rather than individual tax numbers. It explicitly mentions the 4 supported key types, making the purpose highly specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'when processing Brazilian payments, validating payment forms, or any fintech application handling Brazilian transfers.' It also distinguishes it from siblings by covering multiple key types (CPF/CNPJ, email, phone, EVP) rather than single validators like validate_cnpj, offering clear alternatives based on context.

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

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

With no annotations provided, the description carries the full burden. It discloses the tool's behavior by stating it returns 'RFC type (person or company), whether the format is valid, and the cleaned RFC.' However, it doesn't mention error handling, performance characteristics, or authentication requirements, leaving some behavioral aspects undocumented.

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 efficiently structured in two sentences: the first states the tool's purpose and scope, the second provides usage guidelines. Every word contributes value with no redundancy or unnecessary elaboration.

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 no output schema, the description provides good context about what the tool does, when to use it, and what it returns. However, without annotations or output schema, it could benefit from more detail about return format structure or error conditions.

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%, so the schema already fully documents the single parameter. The description adds minimal value beyond the schema by reinforcing the character length differences (13 vs 12 characters), but doesn't provide additional semantic context about parameter usage or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: validating Mexican RFC format for both individuals (13 characters) and companies (12 characters). It specifies the exact resource (Mexican RFC) and action (validation), distinguishing it from sibling tools like validate_cnpj or validate_cpf that handle different tax 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 explicitly lists when to use this tool: 'when processing Mexican invoices (CFDI), tax forms, supplier registrations, or any document requiring a valid Mexican taxpayer identification.' This provides clear context for usage versus alternatives like validate_cnpj for Brazil or validate_rut_cl for Chile.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it validates using the modulo-11 checksum algorithm, accepts both formatted and unformatted input, and returns validation status plus formatted RUT. However, it doesn't mention potential edge cases like invalid formats or error handling, leaving some behavioral aspects unspecified.

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 efficiently structured in two sentences: the first states the purpose and algorithm, the second provides usage guidelines and return values. Every sentence adds value without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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 moderate complexity (single parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, input flexibility, and return values. However, without an output schema, it could benefit from more detail on the return format (e.g., structure of the response), though the current description is adequate for basic use.

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, with the parameter 'rut' fully documented in the schema. The description adds minimal value beyond the schema, only reiterating that it accepts formatted or unformatted input. Since schema coverage is high, the baseline score of 3 is appropriate as the description doesn't 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?

The description clearly states the specific verb 'validates' and resource 'Chilean RUT' with the official algorithm. It distinguishes from sibling tools like validate_cnpj, validate_cpf, and validate_rfc_mx by specifying the Chilean tax identification system, making the purpose unambiguous and differentiated.

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 multiple scenarios for when to use this tool: 'processing Chilean invoices, tax forms, user registrations, e-commerce orders, or any document requiring a valid Chilean tax identification number.' This provides clear context and distinguishes it from sibling tools that validate other country-specific identifiers.

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