Free calculators as MCP tools: finance, taxes, health, units, dates. Search, fetch & compute.
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.8/5 across 3 of 3 tools scored. Lowest: 3.2/5.
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.
computeBInspectCalcula 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?
Sin anotaciones, la descripción debe revelar comportamiento pero solo menciona que devuelve JSON. No informa sobre efectos secundarios, permisos, manejo de errores, o si la operación es de solo lectura o destructiva. 'En vivo' es vago.
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?
Dos oraciones concisas que comunican la acción principal y los parámetros clave sin redundancia. La información está al frente: el propósito primero, luego los detalles.
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?
Para una herramienta con 3 parámetros, sin esquema de salida ni anotaciones, la descripción es adecuada pero no completa. Explica cómo usar inputs, pero no describe la estructura del JSON de retorno más allá de 'resultado calculado', ni cubre casos de error o validación.
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?
La cobertura del esquema es 100%, por lo que la línea base es 3. La descripción agrega valor menor: indica que lang es opcional (default es) y provee ejemplo de formato para inputs, lo que ayuda pero no es crítico.
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?
La descripción indica claramente que la herramienta calcula el resultado de una calculadora en vivo, identificando verbos y recurso. Aunque no diferencia explícitamente de los hermanos, se entiende que calcula mientras get_calculator obtiene definiciones y search_calculators busca.
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?
Se menciona pasar slug y inputs, y referencia get_calculator para los campos, lo que sugiere un flujo de uso. No hay guía explícita sobre cuándo no usarla o alternativas como search_calculators.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_calculatorAInspectDevuelve 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 are provided, so the description must cover behavioral traits. It describes return content but does not mention idempotency, failure modes, or permissions. Adequate but not thorough.
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, front-loaded with purpose and usage advice. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (1 required param, no output schema), the description covers purpose, usage, and return content sufficiently. Could mention error handling or output structure, but not necessary for this simple retrieval.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for 'slug'. The tool description adds little beyond restating the parameter's role, earning a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns calculator details by slug, including inputs and example URL. It distinguishes itself from siblings 'compute' and 'search_calculators' by advising 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 advises to use before compute to know parameters, providing clear context. Could be stronger with explicit when-not-to-use, but the sibling names imply alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_calculatorsAInspectBusca 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?
With no annotations, the description carries the full burden. It describes the search behavior and optional filters, but does not explicitly state that it is read-only or mention any side effects, rate limits, or authentication requirements. However, the nature of 'search' implies non-destructive action.
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 main action. Every word serves a purpose, 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?
The tool has no output schema, but the description explains what is returned (slug) and how it fits into a workflow with siblings. It could mention response structure or pagination, but for a simple search tool, it is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing baseline of 3. The description adds value by giving example keywords and explaining the purpose of the parameters (e.g., returning slug for later use), which helps agents understand the context beyond the schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: search calculators by keyword, with examples. It distinguishes from siblings by specifying that the returned slug is for use 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?
Guidance is explicit about using the returned slug with sibling tools, implying this is a preparatory step. No alternative tools or when-not-to-use scenarios are mentioned, but the workflow context is clear.
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" }]
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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