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meskeIA MCP

by com.meskeia

calcular_profundidad_campo

Read-only

Calcula la profundidad de campo (DoF) para una combinación de focal, apertura, distancia y tipo de sensor. Devuelve la distancia hiperfocal, los límites near/far de enfoque nítido y una clasificación del bokeh. Útil para saber cuánto fondo quedará desenfocado o qué apertura usar en paisaje para máxima nitidez.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sensorYesTipo de sensor: ff = Full Frame (35mm), apsc15 = APS-C Nikon/Sony (factor ×1,5), apsc16 = APS-C Canon (factor ×1,6), m43 = Micro 4/3 (factor ×2,0)
aperturaYesApertura del diafragma en valor f (ej. 2.8 para f/2.8, 8 para f/8)
focal_mmYesFocal de la lente en milímetros (ej. 50 para un 50mm, 85 para un 85mm)
distancia_mYesDistancia de enfoque en metros (ej. 3 para 3 metros, 0.5 para 50 cm)

TDQS

A4.4/5.0
Behavior5/5

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

Annotations set readOnlyHint=true, consistent with a calculation tool. Description and annotations align: no destructive actions, output is computed values. Description discloses returned fields (hyperfocal distance, near/far limits, bokeh classification) 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.

Conciseness5/5

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

Two sentences, no redundant information. First sentence states operation and outputs; second sentence gives usage context. Efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description lists returned values (hyperfocal distance, near/far limits, bokeh classification). Parameter count (4) and schema coverage are adequate. For a calculation tool, the description sufficiently informs the agent about inputs and outputs. Minor gap: no explicit indication of result format or units, but they are implied.

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

Parameters3/5

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

Schema description coverage is 100% with clear parameter descriptions (e.g., sensor enum explained). The tool description adds context by listing parameters in the first sentence but does not enhance understanding beyond what the schema already provides. Baseline 3 for high coverage.

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

Purpose5/5

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

Description clearly states the tool calculates depth of field for given focal length, aperture, distance, and sensor type, specifying outputs (hyperfocal distance, near/far limits, bokeh classification). It uniquely distinguishes from sibling 'calcular' tools which cover different domains (fitness, cooking, etc.).

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

Usage Guidelines4/5

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

Description provides practical use cases: determining background blur or aperture for landscapes. It implies when to use but does not explicitly mention when not to use or list alternatives. Given distinct sibling tools, no alternatives needed, but slight lack of exclusion criteria.

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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools are cleanly separated by domain and purpose, but a few close pairs exist: calcular_camara_lenta and calcular_regla_180_video both touch the 180° shutter rule, and calcular_pace_running and calcular_prediccion_running both produce race projections. The descriptions are detailed enough to resolve the ambiguity, but misselection is possible if they are not read carefully.

Naming Consistency5/5

All tool names follow a consistent Spanish verb_noun snake_case pattern, with calcular_ dominating and the other verbs (convertir_, consultar_, recomendar_, escalar_) used for genuinely different action types. There is no mixed casing or style inconsistency.

Tool Count2/5

42 tools is far above the 25+ threshold and the set spans many unrelated domains such as cooking, fitness, photography, vehicles, dates, and finance. While each individual calculator may be useful, the server is not well-scoped and would be much easier to navigate if split into domain-specific MCP servers.

Completeness3/5

Coverage inside each sub-domain is quite thorough, but there are notable gaps: calcular_kilometraje references calcular_irpf, calcular_cuota_autonomo, and comparar_autonomo_vs_sl, none of which exist in this server. These dangling cross-references can lead an agent to attempt calling unavailable tools.

Resources