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get_weather_forecast

Retrieve weather forecasts for cities in Portugal using IPMA data to plan activities and prepare for conditions.

Instructions

Obter previsão meteorológica para uma cidade específica em Portugal

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesNome da cidade (ex: Lisboa, Porto, Coimbra, Faro, etc.)
daysNoNúmero de dias de previsão (máximo 10)

Implementation Reference

  • Implements the core tool logic: fetches location ID, retrieves forecast data and weather types from IPMA API, formats and limits to specified days, returns formatted text response.
    private async getWeatherForecast(city: string, days: number) {
      try {
        // Primeiro, obter a lista de locais para encontrar o globalIdLocal
        const locationsResponse = await fetch(`${this.baseUrl}/distrits-islands.json`);
        const locationsData = await locationsResponse.json() as ApiResponse<Location>;
        
        const location = locationsData.data.find((loc: Location) => 
          loc.local.toLowerCase().includes(city.toLowerCase())
        );
    
        if (!location) {
          return {
            content: [
              {
                type: "text",
                text: `Cidade "${city}" não encontrada. Use get_locations para ver cidades disponíveis.`
              }
            ]
          };
        }
    
        // Obter previsão para o local encontrado
        const forecastResponse = await fetch(
          `${this.baseUrl}/forecast/meteorology/cities/daily/${location.globalIdLocal}.json`
        );
        const forecastData = await forecastResponse.json() as ApiResponse<WeatherForecast>;
    
        // Obter tipos de tempo para descrições
        const weatherTypesResponse = await fetch(`${this.baseUrl}/weather-type-classe.json`);
        const weatherTypesData = await weatherTypesResponse.json() as ApiResponse<WeatherType>;
    
        const weatherTypes = weatherTypesData.data.reduce((acc: any, item: WeatherType) => {
          acc[item.idWeatherType] = item;
          return acc;
        }, {});
    
        const limitedData = forecastData.data.slice(0, days);
        
        let result = `📍 **Previsão para ${location.local}**\n\n`;
        result += `📍 Coordenadas: ${location.latitude}, ${location.longitude}\n`;
        result += `🕐 Última atualização: ${forecastData.dataUpdate}\n\n`;
    
        limitedData.forEach((day: WeatherForecast) => {
          const weatherDesc = weatherTypes[day.idWeatherType]?.descWeatherTypePT || "Desconhecido";
          result += `📅 **${day.forecastDate}**\n`;
          result += `🌡️ Temperatura: ${day.tMin}°C - ${day.tMax}°C\n`;
          result += `☁️ Condições: ${weatherDesc}\n`;
          result += `🌧️ Probabilidade de precipitação: ${day.precipitaProb}%\n`;
          result += `💨 Vento: ${day.predWindDir}\n\n`;
        });
    
        return {
          content: [
            {
              type: "text",
              text: result
            }
          ]
        };
      } catch (error) {
        const errorMessage = error instanceof Error ? error.message : String(error);
        throw new McpError(ErrorCode.InternalError, `Erro ao obter previsão: ${errorMessage}`);
      }
    }
  • src/index.ts:132-150 (registration)
    Registers the 'get_weather_forecast' tool in the ListToolsRequestSchema handler with name, description, and input schema definition.
    {
      name: "get_weather_forecast",
      description: "Obter previsão meteorológica para uma cidade específica em Portugal",
      inputSchema: {
        type: "object",
        properties: {
          city: {
            type: "string",
            description: "Nome da cidade (ex: Lisboa, Porto, Coimbra, Faro, etc.)"
          },
          days: {
            type: "number",
            description: "Número de dias de previsão (máximo 10)",
            default: 5
          }
        },
        required: ["city"]
      }
    },
  • Defines the input schema for the tool, specifying parameters 'city' (required string) and 'days' (optional number, default 5).
    inputSchema: {
      type: "object",
      properties: {
        city: {
          type: "string",
          description: "Nome da cidade (ex: Lisboa, Porto, Coimbra, Faro, etc.)"
        },
        days: {
          type: "number",
          description: "Número de dias de previsão (máximo 10)",
          default: 5
        }
      },
      required: ["city"]
    }
  • Dispatcher case in CallToolRequestSchema handler that validates input and invokes the getWeatherForecast method.
    case "get_weather_forecast":
      if (!toolArgs?.city) {
        throw new McpError(ErrorCode.InvalidParams, "City parameter is required");
      }
      return await this.getWeatherForecast(toolArgs.city, toolArgs.days || 5);
Behavior2/5

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 mentions the geographic scope ('Portugal') which is useful context, but doesn't describe what the forecast includes (e.g., temperature, precipitation), format of return data, rate limits, authentication needs, or error conditions. For a tool with no annotation coverage, this leaves significant behavioral 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?

The description is a single, efficient sentence in Portuguese that directly states the tool's purpose. There's no wasted language, repetition, or unnecessary elaboration. It's appropriately sized and front-loaded with the essential information.

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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and geographic scope but lacks information about return values, error handling, and differentiation from sibling tools. With no output schema, the description should ideally explain what the forecast returns, but it doesn't.

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 well-documented in the schema. The description adds no parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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's purpose: 'Obter previsão meteorológica para uma cidade específica em Portugal' (Get weather forecast for a specific city in Portugal). It specifies the verb ('Obter' - Get), resource ('previsão meteorológica' - weather forecast), and geographic scope ('Portugal'). However, it doesn't explicitly differentiate from siblings like 'get_weather_warnings' or 'get_uv_forecast' which suggests it's for general forecasts rather than specific warnings or UV data.

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 sibling tools like 'get_weather_warnings' for alerts, 'get_uv_forecast' for UV-specific data, or 'get_weather_stations' for current observations. There's no context about prerequisites, limitations, or typical use cases beyond the basic purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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