Skip to main content
Glama
MCP Weather Server

MCP Weather Server

by adhikasp

MCP 날씨 서버

AccuWeather API를 사용하여 시간별 날씨 예보를 제공하는 간단한 MCP 서버입니다.

설정

  1. uv 사용하여 종속성을 설치합니다:

지엑스피1

  1. AccuWeather API 키로 .env 파일을 만듭니다.

ACCUWEATHER_API_KEY=your_api_key_here

AccuWeather API 에 등록하면 API 키를 받을 수 있습니다.

Related MCP server: Weather-Open-Meteo-MCP

서버 실행

{
    "mcpServers": {
        "weather": {
            "command": "uvx",
            "args": ["--from", "git+https://github.com/adhikasp/mcp-weather.git", "mcp-weather"],
            "env": {
                "ACCUWEATHER_API_KEY": "your_api_key_here"
            }
        }
    }
}

API 사용

시간별 날씨 예보 받기

응답:

{
    "location": "Jakarta",
    "location_key": "208971",
    "country": "Indonesia",
    "current_conditions": {
        "temperature": {
            "value": 32.2,
            "unit": "C"
        },
        "weather_text": "Partly sunny",
        "relative_humidity": 75,
        "precipitation": false,
        "observation_time": "2024-01-01T12:00:00+07:00"
    },
    "hourly_forecast": [
        {
            "relative_time": "+1 hour",
            "temperature": {
                "value": 32.2,
                "unit": "C"
            },
            "weather_text": "Partly sunny",
            "precipitation_probability": 40,
            "precipitation_type": "Rain",
            "precipitation_intensity": "Light"
        }
    ]
}

API는 다음을 제공합니다.

  • 온도, 날씨 설명, 습도, 강수량 상태를 포함한 현재 날씨 상황

  • 다음을 포함한 시간별 데이터가 포함된 12시간 예보:

    • 현재 시간으로부터의 상대 시간

    • 섭씨 온도

    • 날씨 설명

    • 강수 확률, 유형 및 강도

Available Tools

1 tool
get_hourly_weatherC

Get hourly weather forecast for a location.

NameRequiredDescriptionDefault
locationYes

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Get' which implies a read operation, but fails to describe any behavioral traits such as rate limits, authentication needs, error handling, or what the forecast includes. This leaves significant gaps for a tool that likely interacts with external data.

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?

The description is a single, efficient sentence that directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the tool's complexity (weather forecasting likely involves external APIs), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't explain return values, error cases, or operational constraints, leaving the agent poorly informed.

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?

The description adds minimal meaning beyond the input schema, which has 0% coverage. It implies the 'location' parameter is used to specify where to get the forecast, but doesn't clarify format (e.g., city name, coordinates) or constraints. With one parameter and low schema coverage, the description provides some context but doesn't fully compensate.

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

Purpose4/5

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

The description clearly states the action ('Get') and resource ('hourly weather forecast for a location'), making the tool's purpose immediately understandable. It doesn't need to differentiate from siblings since none exist, but it could be more specific about what 'hourly weather forecast' entails (e.g., temperature, precipitation).

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives or in what context. The description states what it does but offers no information about prerequisites, timing, or limitations, leaving the agent without usage direction.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • First observedget_hourly_weather

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.

Naming Consistency5/5

The single tool name follows a consistent verb_noun pattern (get_hourly_weather). Since there is only one tool, there are no deviations or mixed conventions to evaluate, resulting in perfect consistency.

Tool Count2/5

A single tool for a weather server is too few for the apparent scope, as it only provides hourly forecasts without coverage for current conditions, daily forecasts, or other common weather data. This minimal set limits functionality and will likely cause agent failures in broader weather-related tasks.

Completeness2/5

The tool surface is severely incomplete for a weather domain, lacking obvious gaps such as current weather, daily forecasts, alerts, or historical data. With only hourly forecasts, agents cannot perform comprehensive weather-related operations, leading to significant limitations in coverage.

Maintenance

ActivityInactive
ResponsivenessSyncing

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to fetch current weather conditions and forecasts for any city using the Open-Meteo API. Provides temperature, precipitation, and hourly forecast data through natural language queries.
    ISC
  • A
    license
    Not graded
    quality
    D
    maintenance
    Provides hourly weather forecasts using the AccuWeather API, including current conditions and 12-hour forecast with temperature, precipitation, and weather description.
    1 npm
    1
    -