openweather-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@openweather-mcpwhat's the weather in Tokyo?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
openweather-mcp
Open Weather Map API を利用した MCP サーバーです。都市名を指定して現在の天気情報を取得できます。
前提条件
Python 3.11 以上
uv(パッケージ管理ツール)
Related MCP server: Weather MCP Server
uv のインストール
macOS / Linux:
curl -LsSf https://astral.sh/uv/install.sh | shWindows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Homebrew:
brew install uvインストール後、ターミナルを再起動してから uv --version で確認してください。
依存関係のインストール
uv syncOpenWeatherMap API キーの取得
OpenWeatherMap にアクセスし、アカウントを作成する
ログイン後、API keys ページを開く
API キーをコピーする(新規作成も可能)
プロジェクトルートに
.envファイルを作成し、API キーを設定する
cp .env.example .env.env を編集して your-api-key-here を取得した API キーに置き換えてください。
OPENWEATHER_API_KEY=your-api-key-here
OPENWEATHER_BASE_URL=https://api.openweathermap.org/data/2.5Claude Desktop での使い方
Claude Desktop の設定ファイル(claude_desktop_config.json)に以下を追加してください。
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"openweathermap": {
"command": "uv",
"args": [
"--directory",
"/path/to/openweather-mcp",
"run",
"openweather.py"
]
}
}
}/path/to/openweather-mcp は実際のプロジェクトのパスに置き換えてください。
設定後、Claude Desktop を再起動すると get_weather ツールが使えるようになります。チャットで「東京の天気を教えて」のように聞くと、現在の天気情報を返します。
Available Tools
1 toolget_weatherA
指定された都市の現在の天気を取得します
Args:
city: 都市名(例: Tokyo, London, New York)
Returns:
天気情報のフォーマットされた文字列
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It does mention that the return value is a 'フォーマットされた文字列' (formatted string), which is helpful. However, it does not disclose potential errors, units, or any other behavioral nuances. For a simple read operation, this is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with a one-sentence purpose followed by well-structured Args and Returns sections. Every element is necessary and directly serves the user's understanding, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no siblings, the description covers the essential aspects: purpose, parameter semantics, and return format. It falls short only on minor details like units (Celsius vs Fahrenheit) or error behavior, but these are not critical for understanding the core functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for the 'city' parameter (0% coverage), but the description's Args section explicitly explains it as '都市名' (city name) with examples (Tokyo, London, New York). This fully compensates for the schema gap and adds practical meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: '指定された都市の現在の天気を取得します' (Gets the current weather for the specified city). This uses a specific verb (get) and resource (current weather for a city), leaving no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, nor does it mention any prerequisites or context. Since there are no sibling tools, it could have simply stated the use case (e.g., 'Use this to check current conditions'), but it remains implicit at best.
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 tool update
v0.1.0- First observed
get_weather
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or overlap between tools. The tool's purpose is clear and distinct from any potential alternatives.
The single tool follows a standard and clear verb_noun naming pattern (`get_weather`). With only one tool, there is no inconsistency to evaluate, and the name is intuitive and readable.
The server is named `openweather-mcp` but provides only one operation. This feels too few for the apparent scope of a weather service, which typically requires multiple endpoints (e.g., forecasts, air quality, historical data). The set is thin and likely insufficient for typical agent use cases.
The tool surface is severely limited, offering only current weather for a city. For a general weather API, significant operations such as forecasts, multi-day outlooks, or weather alerts are missing, leaving agents with limited capability and causing potential failures when those features are needed.
Maintenance
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