Skip to main content
Glama
enesjakozh
akaramanapp

Weather MCP Server

by akaramanapp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Servidor MCP meteorológico

Un servidor de Protocolo de Contexto de Modelo (MCP) que proporciona información meteorológica y alertas para ubicaciones en EE. UU. mediante la API del Servicio Meteorológico Nacional (NWS). Este servidor está diseñado para ser utilizado por agentes de IA a través del Protocolo de Contexto de Modelo.

Características

  • Herramienta para recibir alertas meteorológicas para cualquier estado de EE. UU.

  • Herramienta para obtener pronósticos meteorológicos detallados para cualquier ubicación de EE. UU. utilizando coordenadas

  • Admite temperatura, condiciones del viento y descripciones breves de pronósticos.

  • Datos en tiempo real del Servicio Meteorológico Nacional

Related MCP server: Weather MCP Server

Prerrequisitos

  • Node.js (v16 o superior)

  • gestor de paquetes npm o yarn

Instalación

  1. Clonar el repositorio:

git clone https://github.com/akaramanapp/weather-mcp-server.git
cd weather-mcp-server

  1. Instalar dependencias:

npm install

  1. Construir la aplicación:

npm run build

Herramientas MCP

El servidor proporciona dos herramientas MCP que pueden utilizar los agentes de IA:

recibir alertas

Reciba alertas meteorológicas para un estado específico de EE. UU.

Parámetros:

  • state : código de estado de dos letras (por ejemplo, CA, NY)

Ejemplo de respuesta:

{
  "content": [
    {
      "type": "text",
      "text": "Active alerts for CA: ..."
    }
  ]
}

obtener pronóstico

Obtenga el pronóstico del tiempo para una ubicación específica utilizando coordenadas.

Parámetros:

  • latitude : Latitud de la ubicación (-90 a 90)

  • longitude : Longitud de la ubicación (-180 a 180)

Ejemplo de respuesta:

{
  "content": [
    {
      "type": "text",
      "text": "Morning: Temperature: 72°F, Wind: 5mph NW, Partly cloudy..."
    }
  ]
}

Estructura del proyecto

weather/
├── src/
│   └── index.ts    # Main server code with MCP tool implementations
├── build/          # Compiled JavaScript files
├── package.json    # Project dependencies and scripts
└── tsconfig.json   # TypeScript configuration

Detalles técnicos

  • Creado con TypeScript

  • Implementa el servidor del Protocolo de Contexto de Modelo (MCP)

  • Utiliza @modelcontextprotocol/sdk para la implementación del servidor MCP

  • Interfaces con la API del Servicio Meteorológico Nacional (NWS)

  • Objetivo ES2022 con resolución de módulo Node16

Desarrollo

Para modificar o ampliar el servidor:

  1. Realizar cambios en el archivo src/index.ts

  2. Reconstruir la aplicación:

npm run build

Dependencias

  • @modelcontextprotocol/sdk: marco de implementación del servidor MCP

  • zod: Comprobación y validación de tipos de tiempo de ejecución para parámetros de herramientas

  • TypeScript: Dependencia de desarrollo para la seguridad de tipos

Licencia

ISC

Notas

  • Este servidor solo funciona para ubicaciones de EE. UU., ya que utiliza la API del Servicio Meteorológico Nacional.

  • Las solicitudes de API tienen una velocidad limitada y requieren un encabezado de agente de usuario

  • Todas las coordenadas deben estar en formato de grados decimales.

  • Esta no es una aplicación CLI independiente, sino un servidor que proporciona herramientas para agentes de IA a través del Protocolo de contexto de modelo.

Available Tools

2 tools
get-alertsC

Get weather alerts for a state

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYesTwo-letter state code (e.g. CA, NY)

TDQS

C2.9/5.0
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 states what the tool does but doesn't describe traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex alert data. It doesn't explain what the alerts include, how they're formatted, or any limitations, leaving the agent with insufficient context for effective use.

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 description doesn't add any parameter-specific information beyond what's in the input schema, which has 100% coverage and fully documents the 'state' parameter. This meets the baseline score of 3, as the schema adequately handles parameter semantics without needing extra description.

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 verb ('Get') and resource ('weather alerts for a state'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling tool 'get-forecast', which likely provides different weather data, so it doesn't reach the highest score.

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 provides no guidance on when to use this tool versus its sibling 'get-forecast' or any alternatives. It lacks context about usage scenarios, exclusions, or prerequisites, offering only a basic statement of function.

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

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYesLatitude of the location
longitudeYesLongitude of the location

TDQS

C2.9/5.0
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 states what the tool does but doesn't describe any behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or what the response format might be. This is inadequate for a tool with zero annotation coverage.

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 a single, clear sentence that efficiently conveys the core purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there are no annotations and no output schema, the description is incomplete. It doesn't provide enough context about behavioral aspects, response format, or how this tool differs from its sibling. For a tool with this level of complexity and lack of structured data, the description should do more to compensate.

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 schema description coverage is 100%, with both parameters (latitude and longitude) well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema, so it meets the baseline score of 3.

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 verb ('Get') and resource ('weather forecast for a location'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get-alerts', which likely provides different weather-related information, so it doesn't achieve the highest score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'get-alerts' or explain the difference between getting a forecast versus alerts, leaving the agent without context for tool selection.

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 updates
    • First observedget-alerts
    • First observedget-forecast
Deploy Server
Context Every Agent Can ShareAgents share what they learn, hand off work, and keep it when you switch models. Start with 5 GB free.fulcradynamics.comAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get-alerts retrieves weather alerts for a state, while get-forecast provides weather forecasts for a location. There is no overlap in functionality, and an agent can easily differentiate between them based on their specific use cases.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern with hyphens (get-alerts and get-forecast). This uniformity makes the tool set predictable and easy to understand, with no deviations in style or convention.

Tool Count2/5

With only two tools, the server feels thin for a weather domain, as it lacks essential operations like getting current conditions, historical data, or radar information. While the tools are well-defined, the count is too low to provide comprehensive coverage for typical weather-related tasks.

Completeness2/5

The tool set is severely incomplete for a weather server, missing core functionalities such as current weather, historical data, and radar or satellite imagery. Agents will face significant gaps when trying to perform common weather queries, leading to potential failures in broader tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • MCP Weather Server

    adarshem
    A
    license
    B
    quality
    F
    maintenance
    A Model Context Protocol server that provides tools to fetch weather alerts for US states and forecasts based on latitude/longitude coordinates using the US National Weather Service API.
    2
    159 npm
    1
    MIT

  • Weather MCP Server

    mauricioTechDev
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables AI models to fetch weather alerts and detailed forecasts for US locations using the National Weather Service API.
    159 npm
    GPL 3.0

Appeared in Searches