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akaramanapp

Weather MCP Server

by akaramanapp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

天気MCPサーバー

米国国立気象局（NWS）APIを使用して、米国各地の気象情報と警報を提供するモデルコンテキストプロトコル（MCP）サーバーです。このサーバーは、モデルコンテキストプロトコルを介してAIエージェントが使用できるように設計されています。

特徴

  • 米国のどの州の天気警報も受信できるツール

  • 座標を使用して米国の任意の場所の詳細な天気予報を取得するためのツール

  • 気温、風の状況、短い予報の説明をサポート

  • 国立気象局からのリアルタイムデータ

Related MCP server: Weather MCP Server

前提条件

  • Node.js (v16 以上)

  • npm または yarn パッケージマネージャー

インストール

  1. リポジトリをクローンします。

git clone https://github.com/akaramanapp/weather-mcp-server.git
cd weather-mcp-server

  1. 依存関係をインストールします:

npm install

  1. アプリケーションをビルドします。

npm run build

MCPツール

サーバーは、AI エージェントが使用できる 2 つの MCP ツールを提供します。

アラートを取得する

特定の米国の州の天気警報を取得します。

パラメータ:

  • state : 2文字の州コード（例：CA、NY）

応答例:

{
  "content": [
    {
      "type": "text",
      "text": "Active alerts for CA: ..."
    }
  ]
}

予測を取得する

座標を使用して特定の場所の天気予報を取得します。

パラメータ:

  • latitude : 場所の緯度 (-90 ～ 90)

  • longitude : 場所の経度 (-180 ～ 180)

応答例:

{
  "content": [
    {
      "type": "text",
      "text": "Morning: Temperature: 72°F, Wind: 5mph NW, Partly cloudy..."
    }
  ]
}

プロジェクト構造

weather/
├── src/
│   └── index.ts    # Main server code with MCP tool implementations
├── build/          # Compiled JavaScript files
├── package.json    # Project dependencies and scripts
└── tsconfig.json   # TypeScript configuration

技術的な詳細

  • TypeScriptで構築

  • モデルコンテキストプロトコル（MCP）サーバーを実装します

  • MCP サーバーの実装には @modelcontextprotocol/sdk を使用します

  • 国立気象局 (NWS) API とのインターフェース

  • Node16モジュール解決によるES2022ターゲット

発達

サーバーを変更または拡張するには:

  1. src/index.tsファイルに変更を加える

  2. アプリケーションを再構築します。

npm run build

依存関係

  • @modelcontextprotocol/sdk: MCP サーバー実装フレームワーク

  • zod: ツールパラメータのランタイム型チェックと検証

  • TypeScript: 型安全性のための開発依存性

ライセンス

ISC

注記

  • このサーバーは国立気象サービスAPIを使用しているため、米国の場所でのみ機能します。

  • APIリクエストはレート制限があり、User-Agentヘッダーが必要です。

  • すべての座標は10進度形式で入力する必要があります

  • これはスタンドアロンのCLIアプリケーションではなく、モデルコンテキストプロトコルを通じてAIエージェントにツールを提供するサーバーです。

Available Tools

2 tools
get-alertsC

Get weather alerts for a state

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYesTwo-letter state code (e.g. CA, NY)

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 states what the tool does but doesn't describe traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.

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 lack of annotations and output schema, the description is incomplete for a tool that likely returns complex alert data. It doesn't explain what the alerts include, how they're formatted, or any limitations, leaving the agent with insufficient context for effective use.

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 doesn't add any parameter-specific information beyond what's in the input schema, which has 100% coverage and fully documents the 'state' parameter. This meets the baseline score of 3, as the schema adequately handles parameter semantics without needing extra description.

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 verb ('Get') and resource ('weather alerts for a state'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling tool 'get-forecast', which likely provides different weather data, so it doesn't reach the highest score.

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?

The description provides no guidance on when to use this tool versus its sibling 'get-forecast' or any alternatives. It lacks context about usage scenarios, exclusions, or prerequisites, offering only a basic statement of function.

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

get-forecastC

Get weather forecast for a location

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYesLatitude of the location
longitudeYesLongitude of the location

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 states what the tool does but doesn't describe any behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or what the response format might be. This is inadequate for a tool with zero annotation coverage.

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, clear sentence that efficiently conveys the core purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent 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 that there are no annotations and no output schema, the description is incomplete. It doesn't provide enough context about behavioral aspects, response format, or how this tool differs from its sibling. For a tool with this level of complexity and lack of structured data, the description should do more to compensate.

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 schema description coverage is 100%, with both parameters (latitude and longitude) well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema, so it meets the baseline score of 3.

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 verb ('Get') and resource ('weather forecast for a location'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get-alerts', which likely provides different weather-related information, so it doesn't achieve the highest score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'get-alerts' or explain the difference between getting a forecast versus alerts, leaving the agent without context for tool selection.

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. 2 tool updates
    • First observedget-alerts
    • First observedget-forecast
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get-alerts retrieves weather alerts for a state, while get-forecast provides weather forecasts for a location. There is no overlap in functionality, and an agent can easily differentiate between them based on their specific use cases.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern with hyphens (get-alerts and get-forecast). This uniformity makes the tool set predictable and easy to understand, with no deviations in style or convention.

Tool Count2/5

With only two tools, the server feels thin for a weather domain, as it lacks essential operations like getting current conditions, historical data, or radar information. While the tools are well-defined, the count is too low to provide comprehensive coverage for typical weather-related tasks.

Completeness2/5

The tool set is severely incomplete for a weather server, missing core functionalities such as current weather, historical data, and radar or satellite imagery. Agents will face significant gaps when trying to perform common weather queries, leading to potential failures in broader tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • MCP Weather Server

    adarshem
    A
    license
    B
    quality
    D
    maintenance
    A Model Context Protocol server that provides tools to fetch weather alerts for US states and forecasts based on latitude/longitude coordinates using the US National Weather Service API.
    2
    55 npm
    1
    MIT

  • Weather MCP Server

    mauricioTechDev
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables AI models to fetch weather alerts and detailed forecasts for US locations using the National Weather Service API.
    55 npm
    GPL 3.0

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