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TimLukaHorstmann

MCP Weather

by TimLukaHorstmann
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TypeScript
Remote

MCP Weather Server

npm version license node version issues weekly downloads Trust Score

A Model Context Protocol (MCP) server that provides hourly and daily weather forecasts using the AccuWeather API.

Quick Start

You need an AccuWeather API key (free tier available).
Sign up here and create an app to get your key.

Export your API key as an environment variable:

export ACCUWEATHER_API_KEY=your_api_key_here

Then run the MCP Weather server directly with:

npx -y @timlukahorstmann/mcp-weather

Or, for HTTP/REST access via supergateway:

npx -y supergateway --stdio "npx -y @timlukahorstmann/mcp-weather" \
  --port 4004 \
  --baseUrl http://127.0.0.1:4004 \
  --ssePath /messages \
  --messagePath /message \
  --cors "*" \
  --env ACCUWEATHER_API_KEY="$ACCUWEATHER_API_KEY"

Related MCP server: Weather MCP Server

MCP Server Config Example

For integration with Claude Desktop or other MCP-compatible clients, add this to your config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "weather": {
      "command": "npx",
      "args": ["-y", "@timlukahorstmann/mcp-weather"],
      "env": {
        "ACCUWEATHER_API_KEY": "your_api_key_here"
      }
    }
  }
}

Overview

This MCP server allows large language models (like Claude) to access real-time weather data. When integrated with an LLM, it enables the model to:

  • Fetch accurate, up-to-date weather forecasts

  • Provide hourly weather data for the next 12 hours

  • Access daily weather forecasts for up to 15 days

  • Display data in both metric (°C) and imperial (°F) units

  • View temperature, conditions, precipitation information, and other weather details

Available Tools

Hourly Weather Forecast

  • Tool name: weather-get_hourly

  • Provides hourly forecasts for the next 12 hours

  • Parameters:

    • location (required): City or location name

    • units (optional): "metric" (Celsius, default) or "imperial" (Fahrenheit)

Daily Weather Forecast

  • Tool name: weather-get_daily

  • Provides daily forecasts for up to 15 days

  • Parameters:

    • location (required): City or location name

    • days (optional): Number of forecast days (1, 5, 10, or 15; default is 5)

    • units (optional): "metric" (Celsius, default) or "imperial" (Fahrenheit)

Prerequisites

  • Node.js ≥18

  • An AccuWeather API key (set via .env or your shell)

Setup

  1. Clone this repository:

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

  2. Install dependencies:

    npm install

  3. Get an AccuWeather API key:

  4. Create a .env file with your API key:

    ACCUWEATHER_API_KEY=your_api_key_here

  5. Build the project:

    npm run build

Usage with Claude Desktop

  1. Configure Claude Desktop to use this MCP server:

    • Open Claude Desktop

    • Go to Settings > Developer > Edit Config

    • Add the following to your claude_desktop_config.json:

    {
  "mcpServers": {
    "weather": {
      "command": "npx",
      "args": ["-y", "@timlukahorstmann/mcp-weather"],
      "env": {
        "ACCUWEATHER_API_KEY": "your_api_key_here"
      }
    }
  }
}

  2. Restart Claude Desktop

  3. In a new conversation, enable the MCP server by clicking the plug icon and selecting "weather"

  4. Now you can ask Claude for weather forecasts, such as:

    • "What's the hourly weather forecast for New York City?"

    • "Give me the 5-day forecast for London."

    • "What will the weather be like in Tokyo this week in Fahrenheit?"

    • "Will it rain in San Francisco tomorrow?"

Development

  • Install dev dependencies: npm install

  • Lint your code: npm run lint

  • Build: npm run build

  • Run tests: npm test

  • Start in dev mode: npm run dev

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Future Enhancements

We're always looking to improve the MCP Weather Server. Here are some features we're considering for future releases:

  • Extended Hourly Forecasts: Beyond 12 hours, e.g., 24 or 48 hours.

  • Weather Alerts: Integration with AccuWeather's severe weather alerts API.

  • Location Autocomplete: Improved location searching with autocomplete suggestions.

  • Historical Weather Data: Access to past weather conditions.

If you have ideas for other features, feel free to open an issue!

Changelog

0.4.0

  • Removed sessionId requirement from all tools as it was not used for anything internally

  • This simplifies integrations and reduces confusion for LLM usage

0.3.0 and earlier

  • Initial releases with basic functionality

License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

2 tools
weather-get_dailyC

Get daily weather forecast for up to 15 days

ParametersJSON Schema
NameRequiredDescriptionDefault
locationYesThe city or location for which to retrieve the weather forecast.
daysNoNumber of days to forecast (1, 5, 10, or 15). Default is 5.
unitsNoTemperature unit system (metric for Celsius, imperial for Fahrenheit). Default is metric.

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 full burden for behavioral disclosure. It states the tool 'gets' data (implying read-only) but doesn't mention rate limits, authentication requirements, data freshness, error conditions, or what the forecast includes (e.g., temperature, precipitation). For a weather API tool with zero 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 that communicates the core purpose without unnecessary words. It's appropriately front-loaded with the main action and scope, making it easy to parse quickly. Every word earns its place in this compact formulation.

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?

For a weather forecasting tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what data the forecast returns (temperature, conditions, etc.), how results are structured, whether there are usage limits, or authentication requirements. The combination of missing behavioral context and output information creates significant gaps for an agent trying to use this tool effectively.

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 adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It mentions 'up to 15 days' which aligns with the 'days' parameter enum, but doesn't provide additional context about parameter interactions, defaults, or practical usage examples. With complete schema documentation, the baseline score of 3 is appropriate.

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 action ('Get daily weather forecast') and scope ('for up to 15 days'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling 'weather-get_hourly' beyond the 'daily' vs 'hourly' naming, missing an opportunity to clarify the distinction in forecast granularity.

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 'weather-get_hourly' or any alternatives. It mentions 'up to 15 days' but doesn't explain when to choose different day counts or why one might prefer daily over hourly forecasts, leaving usage context entirely implicit.

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

weather-get_hourlyA

Get hourly weather forecast for the next 12 hours

ParametersJSON Schema
NameRequiredDescriptionDefault
locationYesThe city or location for which to retrieve the weather forecast.
unitsNoTemperature unit system (metric for Celsius, imperial for Fahrenheit). Default is metric.

TDQS

A3.5/5.0
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 states the tool's function but omits critical behavioral details such as rate limits, authentication requirements, error handling, or response format (e.g., JSON structure, timestamps). For a tool with no annotations, this leaves significant gaps in understanding how it behaves operationally.

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 that front-loads the core purpose without unnecessary words. Every part ('Get hourly weather forecast for the next 12 hours') contributes directly to understanding the tool's function, making it highly concise and well-structured for quick comprehension.

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 low complexity (2 parameters, no output schema, no annotations), the description covers the basic purpose adequately. However, it lacks details on behavioral aspects (e.g., rate limits, auth) and output format, which are important for a tool with no annotations. It's minimally viable but has clear gaps in providing a complete operational context.

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 ('location' and 'units') well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't clarify location formats or unit defaults further). Baseline score of 3 is appropriate as the schema handles parameter documentation adequately.

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

Purpose5/5

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

The description clearly states the specific action ('Get hourly weather forecast') and resource ('weather'), with precise temporal scope ('for the next 12 hours'). It distinguishes from the sibling tool 'weather-get_daily' by specifying hourly vs daily forecasts, making the purpose unambiguous and differentiated.

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 through 'hourly' and 'next 12 hours', suggesting it's for short-term forecasts. However, it lacks explicit guidance on when to use this tool versus the sibling 'weather-get_daily' (e.g., for daily vs hourly needs) or any prerequisites/exclusions, leaving usage decisions partially inferred rather than clearly stated.

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. 2 tool updates
    • First observedweather-get_daily
    • First observedweather-get_hourly
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TDQS

B3.2/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one provides daily forecasts for up to 15 days, while the other provides hourly forecasts for the next 12 hours. There is no overlap in functionality, and an agent can easily differentiate between them based on the time granularity and forecast duration.

Naming Consistency5/5

Both tools follow a consistent naming pattern with a prefix 'weather-' followed by a verb_noun structure (get_daily, get_hourly). This pattern is predictable and enhances readability, making it easy for agents to understand the tool's purpose at a glance.

Tool Count2/5

With only 2 tools, the server feels under-scoped for a weather domain. While the tools cover forecast retrieval, there are obvious gaps such as current weather conditions, historical data, or location-based searches, making the set too thin for comprehensive weather-related tasks.

Completeness2/5

The tool surface is severely incomplete for a weather server. It lacks essential operations like getting current weather, searching locations, or accessing historical data, which are core to weather applications. Agents will face dead ends when trying to perform basic weather queries beyond forecasts.

Maintenance

ActivityInactive
ResponsivenessSyncing

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