Skip to main content
Glama
cogrowersqa

agroclimate-mcp

by cogrowersqa

get_sensor_history

Retrieve temperature, humidity, chill hours, and chill portions from a device over a configurable period and time block size.

Instructions

Obtiene estadísticas de temperatura, humedad, horas frío y porciones frío de un dispositivo. Siempre consulta los últimos 30 días de la API y filtra según el período solicitado. Permite análisis en bloques horarios configurables (por ejemplo 3, 4, 5 o 24 horas). El LLM debe interpretar la pregunta del usuario y pasar fecha_desde y/o fecha_hasta para filtrar. Si no se pasan fechas, muestra estadísticas de las últimas 24 horas por defecto.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dispositivoYesCódigo del dispositivo a consultar (codigo_dispositivo). Obligatorio.
fecha_desdeNoFecha/hora de inicio del filtro en formato ISO 8601 (YYYY-MM-DD o YYYY-MM-DD HH:mm). También acepta: hoy, ayer, esta semana, semana pasada, este mes, mes pasado, este año, año pasado. Opcional. Si no se indica, se asumen las últimas 24 horas.
fecha_hastaNoFecha/hora de fin del filtro en formato ISO 8601 (YYYY-MM-DD o YYYY-MM-DD HH:mm). También acepta: hoy, ayer, esta semana, semana pasada, este mes, mes pasado, este año, año pasado. Opcional. Si no se indica, se asume el momento actual.
intervalo_horasNoTamaño de bloque para análisis de porciones frío (en horas). Ejemplos: 3, 4, 5, 24. Opcional. Por defecto: 3.
Behavior4/5

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

Describes internal behavior: always fetches last 30 days then filters, and defaults to last 24 hours. No annotations exist, so description carries full burden; read-only nature could be more explicit.

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?

Three sentences, front-loaded with purpose, then behavior and guidance. Every sentence contributes meaning without redundancy.

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?

Covers purpose, behavior, and parameter usage well. Missing output format details, but no output schema exists; still adequate for agent to understand tool's function.

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

Parameters4/5

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

Schema covers all parameters (100% coverage). Description adds value by explaining default interval (3 hours), date defaults, and that LLM should interpret user input for date parameters.

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 verb 'Obtiene' (gets) and resource 'estadísticas de temperatura, humedad, horas frío y porciones frío de un dispositivo', distinguishing it from sibling tools like get_devices and get_weather.

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?

Specifies that the LLM should interpret user queries to pass dates, and describes default behavior when no dates provided. However, does not explicitly state when not to use this tool vs alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cogrowersqa/agroclimate-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server