MCP Europe Tools
Server Details
European data validation tools for AI agents. Validates Portuguese NIF, IBAN for 18 European countries, VAT rates for all EU countries, Portuguese public holidays, and European number formatting.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.2/5 across 8 of 8 tools scored.
Each tool has a clearly distinct purpose with no overlap: working day calculation, number formatting, holiday retrieval for two countries, VAT rate lookup, and validation of different European identifiers. The descriptions specify unique domains (e.g., Portugal vs. Spain holidays, IBAN vs. NIF validation), making it easy for an agent to select the correct tool without confusion.
All tool names follow a consistent verb_noun pattern (e.g., calculate_working_days, format_number_european, get_portugal_holidays). The verbs are appropriate and uniform across the set (calculate, format, get, validate), and nouns clearly indicate the target resource or operation, ensuring predictable and readable naming throughout.
With 8 tools, the count is well-scoped for a server focused on European utilities. Each tool earns its place by covering distinct aspects like date calculations, formatting, holiday data, tax rates, and validation, without being overly broad or sparse. This number supports a cohesive set without overwhelming an agent.
The tool set covers key European utility functions such as date handling, number formatting, holiday retrieval, VAT rates, and ID validation, with no dead ends. A minor gap exists in the lack of tools for other European countries beyond Portugal and Spain (e.g., holidays or tax IDs for Germany or France), but agents can work around this with the provided tools for core workflows.
Available Tools
8 toolscalculate_working_daysAInspect
Calculates working days between two dates, excluding weekends and Portuguese public holidays
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | Yes | End date in YYYY-MM-DD format | |
| start_date | Yes | Start date in YYYY-MM-DD format |
Tool Definition Quality
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 clearly states what the tool does (calculates working days with exclusions), but doesn't address important behavioral aspects like whether the calculation is inclusive or exclusive of start/end dates, timezone considerations, or error handling for invalid dates. The description doesn't contradict annotations since none exist.
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, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for a straightforward calculation tool and front-loads the 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?
For a calculation tool with 2 parameters and no output schema, the description covers the basic purpose but lacks important contextual details. It doesn't explain what constitutes a 'working day' beyond exclusions, doesn't specify whether weekends mean Saturday-Sunday or could vary, and doesn't indicate the return format or whether holidays are dynamically fetched from get_portugal_holidays.
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 both parameters with format requirements. The description doesn't add any parameter-specific information beyond what's in the schema. It implies date parameters but doesn't provide additional context about date ranges, boundary conditions, or format variations.
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 with specific verb ('calculates') and resource ('working days'), and distinguishes it from siblings by specifying exclusion criteria (weekends and Portuguese public holidays). It's not a tautology of the name, as it adds important qualifiers about what constitutes 'working days'.
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 this tool (calculating working days between dates with Portuguese holiday exclusions), but doesn't explicitly state when not to use it or name alternatives. For example, it doesn't mention that get_portugal_holidays could be used separately or that format_number_european might be needed for formatting results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
format_number_europeanBInspect
Formats a number according to European locale conventions
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | The number to format | |
| decimals | No | Number of decimal places (default 2) | |
| country_code | Yes | Country code for formatting (PT, ES, DE, FR, etc) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool formats numbers but doesn't describe behavioral traits like whether it's read-only (likely, but not confirmed), error handling for invalid inputs, performance characteristics, or what the output looks like (e.g., string format). For a tool with no annotations, this leaves significant gaps in understanding its operation.
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, efficient sentence: 'Formats a number according to European locale conventions.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a straightforward formatting tool. Every part of the sentence contributes to understanding the tool's 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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on output format, error conditions, or behavioral context. Without annotations or an output schema, the description should do more to explain the result (e.g., returns a formatted string) and usage constraints, but it meets a basic threshold for a simple formatting operation.
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 clear documentation for all parameters (number, decimals, country_code). The description adds no additional semantic meaning beyond what's in the schema—it doesn't explain the significance of 'European locale conventions' in relation to the parameters or provide examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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: 'Formats a number according to European locale conventions.' It specifies the verb ('formats') and resource ('a number'), and while it doesn't explicitly differentiate from siblings, the function is distinct from data retrieval or validation tools like get_portugal_holidays or validate_iban. However, it could be more specific about what 'European locale conventions' entail (e.g., decimal separators, thousands separators).
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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, such as needing a valid country code, or compare it to other formatting tools (though none are listed among siblings). The context is implied (formatting numbers for European locales), but there are no explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_portugal_holidaysBInspect
Returns Portuguese public holidays for a given year
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | The year to get holidays for (e.g. 2026) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the basic function but lacks behavioral details such as data source, update frequency, error handling, rate limits, or response format. For a read-only tool with no annotations, this leaves significant gaps in understanding its operational characteristics.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core functionality, making it easy to understand immediately. 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 no annotations and no output schema, the description is incomplete for a data retrieval tool. It doesn't explain what the return values look like (e.g., list format, holiday details), potential errors, or data freshness. The simplicity of the tool (1 parameter) doesn't compensate for these missing contextual elements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'year' fully documented in the schema. The description adds no additional parameter semantics beyond implying the year is required for filtering. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
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') and resource ('Portuguese public holidays'), with precise scope ('for a given year'). It distinguishes from sibling tools by focusing on holiday data retrieval rather than formatting, tax rates, or validation functions.
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?
No guidance is provided on when to use this tool versus alternatives. While the purpose is clear, there's no mention of prerequisites, limitations, or comparison with other holiday-related tools (though none exist among siblings). The description assumes the context without explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_spain_holidaysBInspect
Returns Spanish national public holidays for a given year
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | The year to get holidays for (e.g. 2026) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns data but doesn't cover aspects like rate limits, error handling, data format, or whether it's a read-only operation. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core functionality without unnecessary words. It directly communicates the tool's purpose, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but lacks depth. It doesn't explain return values or behavioral traits, which are important for a tool with no structured output or annotation support. It meets basic needs but could be more 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 description coverage is 100%, with the single parameter 'year' fully documented in the schema. The description adds no additional meaning beyond what the schema provides, such as format details or constraints. With high schema coverage, 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 action ('Returns') and resource ('Spanish national public holidays for a given year'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_portugal_holidays' beyond the country specification, but the scope is well-defined.
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?
No guidance is provided on when to use this tool versus alternatives. While 'get_portugal_holidays' is a sibling, the description doesn't mention it or other tools like 'calculate_working_days' that might overlap in holiday-related contexts. Usage is implied by the description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_vat_rateCInspect
Returns the VAT rates for a European country
| Name | Required | Description | Default |
|---|---|---|---|
| country_code | Yes | Two-letter country code (e.g. PT, ES, FR, DE) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns VAT rates but doesn't cover key aspects like whether it's a read-only operation, potential rate limits, error handling for invalid inputs, or the format of the returned data. This leaves significant gaps in understanding how the tool behaves in practice.
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, clear sentence that efficiently conveys the tool's purpose without unnecessary details. It's front-loaded and wastes no words, making it easy to grasp quickly. This is an excellent example of conciseness in tool descriptions.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., rate percentages, effective dates), error conditions, or behavioral traits like idempotency. For a tool that likely involves financial data, this omission could lead to confusion or misuse.
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 description doesn't add any parameter semantics beyond what the input schema provides. The schema has 100% coverage, clearly documenting the single required parameter 'country_code' with examples. Since the description doesn't elaborate further, it meets the baseline score of 3, as 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's purpose: 'Returns the VAT rates for a European country.' It specifies the action ('Returns') and the resource ('VAT rates for a European country'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'validate_nif' or 'validate_iban,' which serve different purposes, so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, such as needing a valid country code, or suggest when not to use it (e.g., for non-European countries). Without such context, users might misuse it or overlook better options among the siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_ibanCInspect
Validates an IBAN number from any European country
| Name | Required | Description | Default |
|---|---|---|---|
| iban | Yes | The IBAN to validate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates IBANs but doesn't describe what validation entails (e.g., format checks, checksum verification), error handling, or output format. For a validation tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every part contributing value, earning the highest score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a validation tool. It doesn't explain what the validation returns (e.g., success/failure, error messages) or behavioral aspects like performance or limitations. With low complexity but insufficient disclosure, it falls short of being fully informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'iban' parameter documented as 'The IBAN to validate.' The description adds no additional parameter details beyond this, such as format examples or constraints. Since the schema already provides adequate parameter information, 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 tool's purpose: 'Validates an IBAN number from any European country.' It specifies the verb ('validates') and resource ('IBAN number'), and the geographic scope ('any European country') provides useful context. However, it doesn't explicitly differentiate from sibling tools like 'validate_nif' (which validates a different identifier type), so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'validate_nif' for different validation needs or specify scenarios where IBAN validation is required (e.g., before financial transactions). Without any usage context or exclusions, the score is low.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_nifCInspect
Validates a Portuguese NIF (tax identification number)
| Name | Required | Description | Default |
|---|---|---|---|
| nif | Yes | The Portuguese NIF to validate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It doesn't describe what validation entails (algorithm, format checks, checksum verification), what the expected output format might be, error conditions, or performance characteristics. The description only states what the tool does at the highest level.
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 - a single sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded with the core functionality. Every word earns its place in this minimal but complete statement of 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?
For a validation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a valid NIF, what validation checks are performed, what the return value looks like (boolean, object with details, etc.), or error handling. The agent would need to guess about the tool's behavior and output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, with the single parameter 'nif' clearly documented as 'The Portuguese NIF to validate.' The description adds no additional parameter information beyond what the schema provides, which is acceptable given the high schema coverage but doesn't enhance understanding of the parameter's format 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 with a specific verb ('validates') and resource ('Portuguese NIF'), making it immediately understandable. However, it doesn't distinguish this tool from its sibling 'validate_iban', which performs a similar validation function but for different data 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 no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'validate_iban' tool or explain that this is specifically for Portuguese tax IDs rather than other validation tools. There's no context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_nif_esBInspect
Validates a Spanish NIF (DNI), NIE (foreigner ID) or CIF (company tax number)
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The Spanish NIF, NIE or CIF to validate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it validates (e.g., format checks, algorithm, error handling), what the output looks like (success/failure, error messages), or any limitations (e.g., only validates Spanish IDs). This leaves significant gaps in understanding the tool's 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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and avoids redundancy, making it easy to understand at a glance.
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 complexity (validating multiple ID types) and lack of annotations or output schema, the description is insufficient. It doesn't explain the validation process, return values (e.g., boolean result, detailed errors), or edge cases (e.g., invalid formats). For a validation tool with no structured output, more context is needed to ensure proper 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 schema description coverage is 100%, with the parameter 'id' clearly documented as 'The Spanish NIF, NIE or CIF to validate'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or validation rules. Baseline 3 is appropriate since the schema adequately covers the parameter.
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 with specific verb ('validates') and resources (Spanish NIF, NIE, CIF). It distinguishes from sibling tools like 'validate_iban' by specifying the type of Spanish identification numbers being validated, 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where validation is needed, prerequisites, or how it differs from similar tools like 'validate_nif' (which might handle different formats or regions). Without such context, users must infer usage based on the name alone.
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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