使用 OpenWeatherMap API 提供天气信息的现代代码协议 (MCP) 服务器。
实时天气数据检索
温度的公制单位
详细的天气信息包括：
温度
湿度
风速
日出/日落时间
天气描述
Related MCP server: Open Weather13 MCP Server
Python 3.12 或更高版本
OpenWeatherMap API 密钥
要通过Smithery自动为 Claude Desktop 安装 Weather MCP 服务器：
npx -y @smithery/cli install @CodeByWaqas/weather-mcp-server --client claude克隆存储库
创建虚拟环境：
python -m venv .venv
source .venv/bin/activate # On Windows use: .venv\Scripts\activate安装依赖项：
pip install -e .# claude_desktop_config.json
# Can find location through:
# Claude -> Settings -> Developer -> Edit Config
{
"mcpServers": {
"mcp-weather-project": {
"command": "uv",
"args": [
"--directory",
"/<absolute-path>/weather-mcp-server/src/resources",
"run",
"server.py"
],
"env": {
"WEATHER_API_KEY": "YOUR_API_KEY"
}
}
}
}git clone https://github.com/CodeByWaqas/weather-mcp-server
安装 MCP 服务器依赖项：
cd weather-mcp-server
# Create virtual environment and activate it
uv venv
source .venv/bin/activate # MacOS/Linux
# OR
.venv/Scripts/activate # Windows
# Install dependencies
uv add "mcp[cli]" python-dotenv requests httpx将src/resources/env.example复制到src/resources/.env
将您的 OpenWeatherMap API 密钥添加到.env文件：
WEATHER_API_KEY=your_api_key_here运行 Claude Desktop 并使用 LLM 检索天气信息
该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。
It fetches the latest weather reports for the given city. Args: city (str): The city name for which weather reports are required. Returns: dict: The weather reports for the given city.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool fetches 'latest' reports, implying real-time or recent data, but doesn't disclose behavioral traits like rate limits, error handling, data sources, or whether it's read-only. For a tool with no annotations, this leaves significant gaps in understanding its operation.
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 appropriately sized and front-loaded, with the purpose stated first. The Args and Returns sections are structured but could be more integrated. It avoids unnecessary details, though the formatting with quotes and line breaks is slightly awkward.
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?
Given no annotations, no output schema, and low parameter semantics coverage, the description is incomplete. It lacks information on return format details (beyond 'dict'), error cases, or operational constraints. For a tool fetching external data, this leaves the agent with insufficient context to use it effectively.
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 description adds minimal semantics beyond the input schema. It explains that 'city' is 'The city name for which weather reports are required,' which clarifies the parameter's purpose but doesn't provide format details (e.g., city name conventions) or examples. With 0% schema description coverage and 1 parameter, the baseline is 4, but the description only partially compensates, so a 3 is appropriate.
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 purpose: 'fetches the latest weather reports for the given city.' It specifies the verb ('fetches') and resource ('weather reports'), though it doesn't need to distinguish from siblings since none exist. The purpose is specific and unambiguous.
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. It mentions the city parameter but offers no context about prerequisites, limitations, or typical use cases. With no siblings, this is less critical, but the description still lacks usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
weatherWith only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear, distinct purpose of fetching weather reports for a city.
The single tool name 'weather' is straightforward and descriptive. Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.
A single tool for a weather server is too few for the apparent scope, as it lacks essential operations like forecasts, historical data, or multi-location queries. This minimal set limits functionality and may cause agent failures in broader weather-related tasks.
The tool surface is severely incomplete for a weather domain, missing obvious gaps such as forecasts, alerts, or location-based searches. While it covers basic current weather, agents will struggle with common weather-related workflows due to these significant omissions.
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An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
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