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Snow Day Probability Api MCP Server

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Snow Day Probability Api MCP Server

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Related MCP server: Weather & Climate Intelligence MCP

简介

这是一个使用 FastMCP 自动生成的 MCP 服务器，用于访问 Snow Day Probability Api API。

  • PyPI 包名: bach-snow_day_probability_api

  • 版本: 1.0.0

  • 传输协议: stdio

安装

从 PyPI 安装:

pip install bach-snow_day_probability_api

从源码安装:

pip install -e .

运行

方式 1: 使用 uvx（推荐，无需安装）

# 运行（uvx 会自动安装并运行）
uvx --from bach-snow_day_probability_api bach_snow_day_probability_api

# 或指定版本
uvx --from bach-snow_day_probability_api@latest bach_snow_day_probability_api

方式 2: 直接运行（开发模式）

python server.py

方式 3: 安装后作为命令运行

# 安装
pip install bach-snow_day_probability_api

# 运行（命令名使用下划线）
bach_snow_day_probability_api

配置

API 认证

此 API 需要认证。请设置环境变量:

export API_KEY="your_api_key_here"

环境变量

变量名

说明

必需

API_KEY

API 密钥

是

PORT

不适用

否

HOST

不适用

否

在 Claude Desktop 中使用

编辑 Claude Desktop 配置文件 claude_desktop_config.json:

{
  "mcpServers": {
    "snow_day_probability_api": {
      "command": "python",
      "args": ["E:\path\to\snow_day_probability_api\server.py"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

注意: 请将 E:\path\to\snow_day_probability_api\server.py 替换为实际的服务器文件路径。

可用工具

此服务器提供以下工具:

snow_probability

Snow Day Probability predicts the likelihood of school closures due to snowfall and severe winter weather using real-time weather data and machine learning algorithms.

端点: GET /

参数:

  • zipcode (number) 必需: Example value: 59716

  • zipcode (string) 必需: Example value: 59716

技术栈

  • FastMCP: 快速、Pythonic 的 MCP 服务器框架

  • 传输协议: stdio

  • HTTP 客户端: httpx

开发

此服务器由 API-to-MCP 工具自动生成。

版本: 1.0.0

Available Tools

1 tool
snow_probabilitySnow ProbabilityB

Snow Day Probability predicts the likelihood of school closures due to snowfall and severe winter weather using real-time weather data and machine learning algorithms.

ParametersJSON Schema
NameRequiredDescriptionDefault
zipcodeYesExample value: 59716

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden; it discloses the data source (real-time weather data) and method (machine learning algorithms), which is useful context. However, it omits what the tool returns (a probability value, scale, format), whether it is read-only, latency, or failure behavior for uncovered locations.

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?

A single front-loaded sentence that leads with the predicted outcome and then qualifies the inputs. No filler, no restatement of the name.

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?

For a one-parameter tool with no output schema, the description should at least signal that a location is required and roughly what comes back. It covers the 'what for' but leaves the agent guessing about the required zipcode and the shape of the result.

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?

Schema description coverage is 100% and there is only one parameter, so the baseline is 3. The description adds nothing about the zipcode input — it does not confirm that a location is required or explain the accepted value format beyond the schema's single example.

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?

States a specific verb and resource: predicting the likelihood of school closures due to snowfall and severe winter weather. The purpose is clear from the first clause, though there are no siblings to differentiate against and the description never ties the purpose to its geographic input.

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 gives no explicit when-to-use guidance, prerequisites, or exclusions. An agent can loosely infer the use case from the purpose sentence, but nothing in the text states the conditions under which this tool should be selected.

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 updatev1.0.0
    • First observedsnow_probability
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

B3.2/5.0

Scored across 1 tool

Disambiguation4/5

With only one tool there is no risk of misselection between competing tools. However, there is nothing to distinguish against, so this is a trivially clean but uninformative case.

Naming Consistency4/5

The single tool name 'snow_probability' uses a readable snake_case noun pattern with no competing conventions. A pattern cannot really be assessed from one name, so it is only nominally consistent.

Tool Count2/5

A single tool is too thin for a service that implies location/date inputs and historical or forecasted variants. The rule of thumb treats one tool as an extreme under-scope, though the domain is genuinely narrow.

Completeness2/5

There is only a prediction operation with no visible parameters, no location lookup, no date/range handling, and no complementary queries. Agents will likely hit dead ends for anything beyond a bare prediction call.

Maintenance

ActivityInactive
ResponsivenessNo issues

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