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Glama

ACTION_GET_WEATHER

Fetches current weather by latitude and longitude using Open-Meteo without an API key, returning temperature, wind speed, wind direction, and WMO weather code for automation workflows.

Instructions

按经纬度查当前天气(Open-Meteo,免 key)(需网络)。输出:weather_success, weather_error, weather_temperature, weather_wind_speed, weather_wind_direction, weather_code, weather_json。温度 ℃、风速 km/h;连接/读取超时固定各 10 秒。weather_code 为 WMO 代码:0 晴,1–3 多云/阴,45/48 雾,51–67 毛毛雨/雨,71–77 雪,80–82 阵雨,85/86 阵雪,95–99 雷暴。。字符串参数支持 ${var} 插值;结果以变量快照 JSON 返回。

Input Schema

NameRequiredDescriptionDefault
requireNo执行后要求为 true 的变量名(如 click_success),不满足按出错处理
latitudeYes纬度 -90–90,支持 ${var}
on_errorNo节点出错时:skip=跳过继续(默认),stop=停止整个工作流
longitudeYes经度 -180–180,支持 ${var}
output_varNo原始响应 JSON 存到的变量名(默认 weather_json)
node_timeout_msNo单节点超时毫秒,0=不限;默认 30 分钟

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries the full load and does so: it discloses the provider, that no API key is needed, the network prerequisite, fixed 10s connect/read timeouts, the exact output variable names, and that results are returned as a variable-snapshot JSON. It also decodes the weather_code field, which is genuinely behavior-level information an agent could not infer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Front-loaded with the core action, then progressively discloses outputs, units, timeouts, and the WMO code table — every block is useful. Minor blemishes: a duplicated period ('。。'), a stray trailing sentence, and the full output-variable enumeration is slightly bulky.

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

Completeness5/5

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

There is no output schema, so the description must explain return values, and it enumerates every returned variable plus units (℃, km/h) and the WMO code semantics. Combined with error variables and timeout behavior, an agent has everything needed to call and interpret this tool.

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 coverage is 100%, so the schema already documents latitude/longitude ranges, on_error, require, and output_var. Beyond that the description explains that string parameters accept ${var} interpolation and that weather_json is the default sink for the raw response, adding operational meaning on top of the schema text.

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?

States a specific verb and resource: fetch current weather by latitude/longitude, and names the backing provider (Open-Meteo, key-free). This clearly separates it from siblings such as ACTION_GET_LOCATION (device coordinates) or ACTION_GEOCODE (place name to coordinates).

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

Usage Guidelines3/5

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

The description implies context (you must already have coordinates, and network access is required) but never says when to prefer this over ACTION_GET_LOCATION, ACTION_GEOCODE, or ACTION_HTTP_REQUEST for arbitrary weather APIs. Usage is inferable rather than stated.

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