US Weather MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@US Weather MCP Serverwhat's the forecast for New York City this weekend?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP de Clima do Tempo para os EUA 🌤️
Servidor MCP (Model Context Protocol) que fornece informações sobre o clima dos Estados Unidos.
📋 Pré-requisitos
Node.js (versĂŁo 14 ou superior)
npm (gerenciador de pacotes do Node.js)
Related MCP server: Weather MCP Server
🚀 Instalação
Clone o repositĂłrio:
git clone https://github.com/FelipeCararo/mcp-intro.git
cd mcp-introInstale as dependĂŞncias:
npm installđź”§ Como Usar
Compilar o projeto
Para compilar o cĂłdigo TypeScript:
npm run buildExecutar o servidor
ApĂłs compilar, execute o servidor MCP:
npm startOu execute diretamente o comando weather:
./build/index.jsScript de inicialização
VocĂŞ tambĂ©m pode usar o script shell incluĂdo:
./start-mcp.sh📦 Estrutura do Projeto
mcp-intro/
├── src/ # Código fonte TypeScript
├── build/ # Código compilado (gerado após build)
├── package.json # Configurações e dependências
├── tsconfig.json # Configurações do TypeScript
└── README.md # Este arquivo🛠️ Tecnologias Utilizadas
TypeScript - Linguagem de programação
@modelcontextprotocol/sdk - SDK do Model Context Protocol
Zod - Validação de schemas e tipos
📝 Scripts DisponĂveis
npm run build- Compila o cĂłdigo TypeScriptnpm start- Executa o servidor compiladonpm test- Executa os testes (ainda nĂŁo implementado)
🤝 Contribuindo
Sinta-se Ă vontade para abrir issues e pull requests no repositĂłrio.
📄 Licença
ISC
đź”— Links
Available Tools
2 toolsget_alertsC
Get weather alerts for a state
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code (e.g. CA, NY) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention any behavioral traits such as rate limits, authentication needs, response format, or error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 a single, clear sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it efficient and easy to parse.
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 the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., alert types, severity levels, timeframes) or any behavioral context needed for effective use, which is insufficient for a tool with no structured support.
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 input schema has 100% description coverage, fully documenting the single parameter 'state' with its format and constraints. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
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 verb ('Get') and resource ('weather alerts for a state'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_forecast', which likely provides different weather data, so it misses full sibling distinction.
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 like 'get_forecast'. It lacks any context about prerequisites, exclusions, or comparative use cases, leaving the agent to infer usage based on tool names alone.
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
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude of the location | |
| longitude | Yes | Longitude of the location |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but lacks critical details like whether this is a read-only operation, potential rate limits, authentication requirements, or what the forecast output includes (e.g., time range, weather elements).
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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded with the core purpose.
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?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the forecast returns (e.g., temperature, precipitation, timeframe), behavioral constraints, or how it differs from the sibling tool, leaving significant gaps in understanding.
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?
Schema description coverage is 100%, with both parameters clearly documented in the schema. The description adds no additional parameter information beyond implying location-based input, so it meets the baseline score when the schema does the heavy lifting.
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 verb 'Get' and resource 'weather forecast for a location', making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_alerts', which likely serves a related but distinct function in weather data retrieval.
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 the sibling 'get_alerts'. There's no mention of prerequisites, alternatives, or specific contexts where this tool is preferred, leaving the agent without usage direction.
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
v1.0.0- First observed
get_alerts - First observed
get_forecast
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one retrieves weather alerts for a state, while the other provides forecasts for a location. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool based on the user's request.
Both tools follow a consistent verb_noun pattern with 'get_' as the prefix, ensuring predictability and readability. The naming style is uniform across the toolset, with no deviations or mixed conventions.
With only two tools, the server feels thin for a weather domain, as it lacks essential operations like current conditions, historical data, or radar information. This minimal set may limit an agent's ability to handle comprehensive weather-related queries effectively.
The tool surface is significantly incomplete for a weather server, missing core functionalities such as current weather, radar maps, or severe weather details. While alerts and forecasts are useful, the absence of these key operations creates notable gaps that could lead to agent failures in broader scenarios.
Maintenance
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- AlicenseBqualityDmaintenanceA 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.295 npm1MIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables AI models to fetch weather alerts and detailed forecasts for US locations using the National Weather Service API.95 npmGPL 3.0
- FlicenseNot gradedqualityDmaintenanceProvides real-time weather data including temperature, humidity, and conditions for any city through the Model Context Protocol.4-
- FlicenseNot gradedqualityDmaintenanceProvides real-time weather alerts and forecasts from the National Weather Service for US locations, integrating with AI assistants via the Model Control Protocol.-