Basic MCP Weather Tool
Implemented as a Node.js application, using Node.js runtime to host the MCP server that provides the weather tool functionality.
Click on "Install 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., "@Basic MCP Weather Toolwhat's the weather like in Tokyo right now?"
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.
🛠️ Basic MCP Demo - Weather Tool
Este proyecto es un ejemplo básico de un servidor MCP (Model Context Protocol) implementado en Node.js. Define una herramienta simple para consultar información del clima de una ciudad utilizando la API pública de Open-Meteo.
📦 Características
Servidor MCP compatible utilizando
@modelcontextprotocol/sdk.Herramienta
fetch-weatherque permite consultar el clima de una ciudad.Ejemplo educativo, ideal para entender el flujo básico de MCP.
Sin claves API privadas, solo usa servicios públicos gratuitos.
Related MCP server: Weather MCP
🚀 Requisitos
Node.js (v18 o superior recomendado)
Acceso a Internet para consultar las APIs públicas
⚙️ Instalación
Clona el repositorio:
git clone https://github.com/itsPatH/basicMCP.git cd basicMCPInstala las dependencias:
npm installEjecuta el servidor MCP:
npx ts-node main.ts Nota: Este servidor espera ser utilizado por un cliente MCP compatible (como Claude con soporte MCP habilitado). Si no tienes un cliente MCP, el servidor quedará esperando conexión.
🌐 Alternativa para Pruebas Locales (Opcional)
Puedes extender este proyecto para agregar un servidor HTTP local y probar la herramienta sin un cliente MCP. Ejemplo de esto disponible en la rama local-test.
⚠️ Advertencias
⚡ Este proyecto es solo para fines educativos. ⚡ No está pensado para entornos de producción. ⚡ Si amplías el proyecto, no subas archivos con claves privadas o información sensible.
📄 Licencia
Este proyecto está bajo la licencia MIT. Eres libre de usarlo, modificarlo o distribuirlo para aprendizaje.
✨ Créditos
Documentación oficial de Model Context Protocol: https://github.com/modelcontextprotocol .
Uso de APIs públicas de Open-Meteo.
Available Tools
1 toolfetch-weatherC
Tool to fetch weather information
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'fetch' but does not disclose behavioral traits like whether it requires authentication, has rate limits, returns errors for invalid cities, or provides data freshness. This leaves significant gaps in understanding the tool's operation.
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 with zero waste, making it appropriately sized. However, it lacks front-loading of critical details like scope or constraints, which could improve structure for clarity.
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 tool's complexity (simple fetch operation), no annotations, and no output schema, the description is incomplete. It does not explain what weather information is returned (e.g., temperature, humidity), potential errors, or any behavioral context, leaving the agent with insufficient information for reliable use.
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, with the 'city' parameter documented as 'City name'. The description adds no additional meaning beyond this, such as format examples (e.g., 'New York' vs. coordinates) or handling of edge cases. Baseline 3 is appropriate since 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 'Tool to fetch weather information' states a general purpose (fetching weather) but is vague about what specific information is retrieved (e.g., current conditions, forecast, temperature). It distinguishes from no siblings since none exist, but lacks specificity in verb+resource details beyond the basic action.
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, such as for real-time data vs. historical data, or any prerequisites like location availability. With no sibling tools, there are no alternatives to differentiate from, but it still lacks basic context for usage.
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. Dates show when Glama detected each change.
1 tool update
- First observed
fetch-weather
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'fetch-weather' has a clear and distinct purpose, making it impossible for an agent to misselect among non-existent alternatives.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'fetch-weather' follows a verb_noun pattern, and with no other tools to compare against, there are no deviations or inconsistencies in naming conventions.
A single tool for a weather server is too few for the apparent scope, as weather-related operations typically involve more than just fetching data (e.g., forecasts, historical data, or location-based queries). This minimal set feels thin and limits functionality, indicating a mismatch with the domain's needs.
The tool surface is severely incomplete for a weather domain. While 'fetch-weather' provides basic retrieval, there are obvious gaps such as lacking forecast tools, historical data access, or location-specific queries. This will likely cause agent failures when more comprehensive weather information is needed.
Maintenance
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