Free calculators as MCP tools: finance, taxes, health, units, dates. Search, fetch & compute.
Scored across 3 tools
Each tool has a unique and clear purpose: search finds calculators, get_calculator retrieves details, and compute performs calculations. No overlap exists.
All tools use lowercase with underscores and follow a verb_noun pattern (search_calculators, get_calculator, compute). Consistent naming convention.
Three tools is ideal for this domain: discover, inspect, and compute. Each tool earns its place without unnecessary bloat.
The tool surface covers the full workflow: searching for calculators, retrieving their input schema, and computing results. No obvious gaps.
Calcula el resultado de una calculadora en vivo. Pasá el slug y un objeto inputs con los campos (ver get_calculator). Opcional lang (es|en|pt). Devuelve el resultado calculado en JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | idioma del resultado (default es) | |
| slug | Yes | slug de la calc (ej. calculadora-imc) | |
| inputs | Yes | pares campo→valor, ej. {"peso":80,"altura":180} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'en vivo' (live calculation) but does not disclose whether the tool has side effects, requires authentication, or has rate limits. For a computation tool, it likely is read-only, but that is not stated explicitly.
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, no wasted words. First sentence states purpose, second sentence details parameters and return type. 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?
No output schema, so description must explain return. It states 'returns result in JSON' but does not describe structure or error cases. Given the tool's complexity (nested inputs, three params), more detail would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions. The description adds value by explaining the relationship to get_calculator for inputs, and that lang is optional. This provides context 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 it calculates a result for a calculator in real-time, requiring a slug and inputs object. It references get_calculator for field definitions, and mentions optional lang. This distinguishes it from siblings get_calculator and search_calculators.
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 tells when to use it (compute a result) and how: pass slug and inputs, optionally lang, and references get_calculator for input fields. It lacks explicit when-not-to-use, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Devuelve la ficha de una calculadora por slug: qué inputs toma (id, tipo, requerido, valores válidos) y una URL de ejemplo. Usalo antes de compute para saber qué parámetros pasar.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | slug de la calc (ej. calculadora-imc) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the return content (inputs, example URL) but does not mention side effects, permissions, or response format. Adequate but not comprehensive.
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 in Spanish, concise and directly to the point. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (1 param, no output schema, no annotations), the description adequately covers purpose and usage. However, it lacks details about the output structure (e.g., JSON format) which could be useful for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'slug' and its description. The tool description adds context by explaining what the tool returns about the calculator, but does not add significant 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 clearly states the tool returns a calculator details by slug, listing what it provides (inputs, example URL). It distinguishes from the sibling tool 'compute' by indicating its use before compute.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Usalo antes de compute para saber qué parámetros pasar.' This directly tells when to use it. It does not mention the other sibling 'search_calculators', but the purpose implies usage when slug is known.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Busca calculadoras de Hacé Cuentas por palabra clave (ej. "imc", "préstamo", "aguinaldo"). Devolvé el slug para usar luego con get_calculator o compute. Opcional: filtrar por category y limitar resultados.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | máximo de resultados (default 10) | |
| query | Yes | palabras clave a buscar | |
| category | No | filtro opcional de categoría (finanzas, salud, deportes, impuestos, etc.) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It states that the tool returns a slug for later use but does not disclose any behavioral traits such as rate limits, authentication, or side effects. It adequately conveys the read-like nature but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with front-loaded purpose and examples. No unnecessary words. Very efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 params, no output schema), the description covers the key points: what it does, what to do with the result, and optional filters. It could elaborate on the return structure, but it explicitly mentions returning a slug, which is 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 coverage is 100%, so the schema already describes all parameters. The description adds context by mentioning optional filters and giving keyword examples, but does not provide additional semantic detail beyond what the schema offers.
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: search calculators by keyword, with examples provided. It distinguishes from siblings by explaining that the result (slug) is to be used with get_calculator or compute.
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 guidance on the workflow: search first, then use the returned slug with sibling tools. It also mentions optional filters. It does not explicitly state when not to use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
computeget_calculatorsearch_calculatorsFinancial calculators over MCP: compound interest, FIRE, historical returns, deep links, 6 languages
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