A
licenseNot graded
qualityD
maintenanceProvides weather alerts and forecasts for US locations using the National Weather Service API.
70 npm
GPL 3.0
Weather MCP Server
天气 MCP 服务器
一个使用美国国家气象局 (NWS) API 提供美国天气信息的模型上下文协议 (MCP) 服务器。基于 FastMCP 构建。
功能
此服务器公开了允许 AI 助手获取实时天气数据的工具：
get_alerts(state: str)：获取指定美国州（使用 2 字母州代码，如 CA、NY、TX）的有效天气警报。返回有关事件、严重程度、描述和安全说明的详细信息。get_forecast(latitude: float, longitude: float)：获取特定位置（使用纬度和经度）的详细天气预报。返回未来 5 个时段的预报（例如：今天下午、今晚、明天）。
Related MCP server: weather-mcp
先决条件
Python 3.10 或更高版本
uv包管理器
安装与设置
本项目使用 uv 进行依赖管理。设置步骤如下：
克隆仓库
安装依赖（如果使用 uv 运行，这将创建一个虚拟环境）：
uv sync
使用方法
你可以通过标准输入/输出手动运行 MCP 服务器，这也是 MCP 客户端与其交互的方式：
uv run weather.py与 Claude Desktop 集成
要将此服务器与 Claude Desktop 一起使用，请将其添加到你的 claude_desktop_config.json 中：
{
"mcpServers": {
"weather": {
"command": "uv",
"args": [
"--directory",
"path/to/your/mcp-server",
"run",
"weather.py"
]
}
}
}与自定义 MCP 客户端集成
如果你有自定义的 Python MCP 客户端，可以按如下方式连接：
from mcp import StdioServerParameters
server_params = StdioServerParameters(
command="uv",
args=["--directory", "path/to/mcp-server", "run", "weather.py"],
env=None
)
# Pass server_params to your MCP client使用的 API
美国国家气象局 API（无需 API 密钥）
Available Tools
2 toolsget_alertsA
Get weather alerts for a US state.
Args: state: Two-letter US state code (e.g. CA, NY)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. 'Get' implies a read-only operation, but the description does not explicitly state side-effect-free behavior or any caveats about alert types or data source. It is not misleading, but it adds minimal behavioral context beyond what the name implies.
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 extremely concise, with a front-loaded purpose statement followed by a compact Args block. Every sentence earns its place and there is no filler.
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?
With a single well-documented parameter, an output schema, and no siblings, the description plus schema fully covers what an agent needs to invoke the tool correctly. No missing context for this simple operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description fully compensates by specifying the parameter format ('Two-letter US state code') and providing concrete examples ('CA, NY'). This adds real meaning beyond the raw schema type string.
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?
The description states a specific verb ('Get'), a clear resource ('weather alerts'), and a clear scope ('US state'). It is unambiguous and leaves no doubt about what the tool does, even without siblings to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There are no sibling tools, so explicit routing guidance is unnecessary. The description clearly implies usage: when you need weather alerts for a US state. It lacks explicit exclusions, but nothing is misleading or missing for a tool of this simplicity.
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.
Args: latitude: Latitude of the location longitude: Longitude of the location
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must carry behavioral context. It only says 'get' a forecast and gives no indication of units, time range, coordinate format, or whether this is a read-only operation. Nothing contradicts annotations, but little is disclosed beyond the basic action.
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?
The description is short and the core purpose is front-loaded. The Args block is somewhat redundant with the schema but does not add significant bloat, keeping the overall entry compact.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides the essential call requirements (latitude and longitude) and the presence of an output schema reduces the need to document return values. However, it omits practical context like expected coordinate units, available forecast periods, and why an agent would choose this over get_alerts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The Args section repeats the parameter names with minimal glosses ('Latitude of the location'), adding almost no meaning beyond the schema titles. Since schema description coverage is 0%, the description should compensate with coordinate format or range details, but it does not.
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 uses a clear verb and resource: 'Get weather forecast for a location'. It does not explicitly mention the sibling get_alerts, but the forecast-vs-alerts distinction is clear enough from the domain.
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 given about when to use this tool instead of get_alerts, nor are any exclusions or alternative conditions provided. The intended usage is only implied by the tool name and description.
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.
2 tool updates
v0.1.0- First observed
get_alerts - First observed
get_forecast
TDQS
B3.4/5.0
Scored across 2 tools
Disambiguation5/5
get_alerts and get_forecast have clearly distinct purposes: one for weather alerts by state, the other for forecast by coordinates. No overlap exists.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern (get_alerts, get_forecast), making them predictable.
Tool Count3/5
With only 2 tools, the server feels thin for a weather domain, bordering on insufficient scope.
Completeness2/5
Common weather operations like current conditions, hourly forecast, or radar are missing, leaving significant gaps for typical use cases.
Maintenance
ActivityInactive
ResponsivenessNo issues
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Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides US weather alerts and forecasts via the National Weather Service API.70 npmGPL 3.0
- AlicenseNot gradedqualityDmaintenanceProvides weather alerts and forecasts for US locations using the National Weather Service API.GPL 3.0
- AlicenseNot gradedqualityDmaintenanceProvides weather alerts and forecasts for US locations using the National Weather Service API.70 npmGPL 3.0