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Weather MCP Server

by penguyen72
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Weather MCP Server

A Model Context Protocol (MCP) server that provides weather forecast and alert information for US locations. Referenced these docs.

Features

  • Weather Forecasts: Get detailed weather forecasts for any location using latitude/longitude coordinates

  • Weather Alerts: Retrieve active weather alerts for US states

  • Real-time data from the National Weather Service

Related MCP server: Weather MCP Server

Installation

Prerequisites

  • Python 3.10 or higher

Setup

  1. Clone this repository:

git clone https://github.com/penguyen72/python-mcp
cd python-mcp

Usage

Configuration

Add the server to your MCP client configuration. For Claude Desktop, add this to your config file:

MacOS: 

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows: 

code $env:AppData\Claude\claude_desktop_config.json

Config:

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
        "run",
        "weather.py"
      ]
    }
  }
}

Available Tools

weather:get_forecast

Get weather forecast for a specific location.

Parameters:

  • latitude (number, required): Latitude of the location

  • longitude (number, required): Longitude of the location

Example:

{
  "latitude": 38.5816,
  "longitude": -121.4944
}

weather:get_alerts

Get active weather alerts for a US state.

Parameters:

  • state (string, required): Two-letter US state code (e.g., "CA", "NY", "TX")

Example:

{
  "state": "CA"
}

API Data Source

This server uses the National Weather Service API to retrieve weather data for US locations.

Available Tools

2 tools
get_alertsB

Get weather alerts for a US state.

Args:
    state: Two-letter US state code (e.g. CA, NY)
ParametersJSON Schema
NameRequiredDescriptionDefault
stateYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It doesn't mention return format, pagination, whether alerts are current or historical, severity levels, or any rate limits or auth requirements. For a read-type tool the lack of annotations leaves significant gaps.

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?

Extremely concise and well-scoped. The description is two lines with an args docstring for the single parameter. Every word is functional with zero waste.

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?

An output schema exists, which reduces the burden for explaining return values. However, with 0% schema description coverage, 1 single param (already covered in the args), no annotations, and no behavioral details (alert types, severity, expiration, update frequency), the description is minimal. For a weather alert tool, agents would benefit from knowing what constitutes an alert return vs. a no-alert return.

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 single parameter 'state' is described in the argument docstring as a two-letter US state code with examples (CA, NY), which adds meaning beyond the bare schema. However, it doesn't specify case sensitivity, whether territories are included (e.g., DC, PR), or what happens for invalid state codes. The examples help but full semantics aren't covered.

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 tool gets weather alerts for a US state, with a specific verb ('get') and resource ('weather alerts') and a geographic scope ('US state'). It distinguishes from the sibling tool 'get_forecast' by the resource type, though it doesn't explicitly name the sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('for a US state') but provides no when-to-use guidance, no exclusions, and no comparison to the sibling get_forecast tool. The intent is reasonably clear but the agent doesn't know when alerts vs forecast is more appropriate.

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
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.4/5.0
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 doesn't disclose how many days of forecast are returned, data freshness, caching behavior, rate limits, or what units (Celsius/Fahrenheit) are used. The description only restates the parameters without adding behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief but under-specified rather than appropriately concise. The Args section is mostly unnecessary given the input schema repeats the same parameter names and types. The single opening sentence is useful, but the parameter documentation is redundant with the schema.

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?

There is an output schema present, which helps, but the description still fails to communicate what the forecast contains or how comprehensive it is. For a tool that returns weather data, the agent has no sense of forecast length, granularity, or data fields without inspecting the output schema. The description is minimally sufficient for a basic 2-param lookup tool but leaves key context undocumented.

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?

Schema description coverage is 0%, meaning the description must compensate. The description merely restates that latitude/longitude are coordinates of the location, which adds marginal value over the schema. It doesn't explain valid ranges (e.g., -90 to 90, -180 to 180), precision requirements, or format expectations. Minimal semantic addition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Get weather forecast for a location' with a clear verb+resource. It doesn't distinguish from its sibling tool get_alerts, and 'forecast' vs 'alerts' distinction is implied but not explicit. The purpose is clear but missing scope details (time range, units).

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 on when to use this tool vs get_alerts. The description doesn't explain the distinction between getting a forecast and getting alerts, nor does it mention any special circumstances (e.g., use get_alerts for severe weather warnings). No when/when-not guidance is provided.

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 updatesv1.0.0
    • Changedget_alerts1 field changed
      • addedInput schema / title
        Added value: +"get_alertsArguments"
    • Changedget_forecast1 field changed
      • addedInput schema / title
        Added value: +"get_forecastArguments"
  2. 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

B3.3/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_alerts retrieves weather alerts for US states, while get_forecast provides forecasts for geographic coordinates. There is no overlap in functionality or ambiguity about which tool to use for each task.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (alerts, forecast). The naming is perfectly uniform and predictable across the tool set.

Tool Count2/5

With only 2 tools, the server feels underpowered for a weather domain. While alerts and forecasts are core functions, there are obvious gaps like current conditions, historical data, or radar imagery that would be expected in a weather API. The minimal tool count limits the server's usefulness.

Completeness2/5

The tool surface is severely incomplete for a weather server. It lacks current conditions, historical weather data, radar/satellite imagery, air quality information, and marine forecasts. The two provided tools cover only narrow aspects of weather data, leaving significant gaps that will hinder agent workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

    x8yuv
    A
    license
    Not graded
    quality
    D
    maintenance
    Provides weather forecasts and alerts for US locations using the National Weather Service API. Supports getting detailed forecasts by coordinates and active weather alerts by state code.
    102 npm
    GPL 3.0

  • Weather MCP Server

    nitvob
    F
    license
    B
    quality
    D
    maintenance
    Provides real-time weather forecasts and alerts for US locations using the National Weather Service API. Supports querying forecasts by coordinates and retrieving active weather alerts by state.
    2
    -