Skip to main content
Glama
first-it-consulting

Weather MCP Server

by first-it-consulting
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Weather MCP Server

A Model Context Protocol (MCP) server that provides weather information using the WeatherAPI service. This server enables AI assistants to fetch current weather, forecasts, and search for locations through stdio communication.

Features

  • Current Weather: Get real-time weather conditions for any location

  • Weather Forecast: Retrieve weather forecasts up to 14 days ahead

  • Location Search: Find and validate location names

  • Air Quality Data: Optional air quality information

  • Debug Logging: Comprehensive logging for troubleshooting

  • Stdio Communication: Direct MCP protocol communication via stdin/stdout

Related MCP server: MCP Weather Server

Prerequisites

  • Python 3.13+

  • uv package manager

  • WeatherAPI account and API key

Installation

Option 1: Via Smithery (Recommended)

Install automatically via Smithery MCP registry:

npx -y @smithery/cli install weather-mcp-server --client claude

Or for other MCP clients:

npx -y @smithery/cli install weather-mcp-server

This will automatically:

  • Install the weather MCP server

  • Add it to your MCP client configuration

  • Prompt you for your WeatherAPI key

Option 2: Manual Installation

  1. Clone or download this repository

  2. Get a WeatherAPI key:

    • Sign up at https://www.weatherapi.com

    • Get your free API key from the dashboard

    • ⚠️ Keep your API key secure - never commit it to version control

  3. Install dependencies:

    uv sync

Configuration

Environment Variables

Set your WeatherAPI key:

export WEATHER_API_KEY=your_api_key_here

Optional - Enable debug logging:

export DEBUG=true

MCP Client Configuration

Add this server to your MCP client configuration (e.g., config.json):

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/weather-mcp-server",
        "run",
        "server.py"
      ],
      "env": {
        "WEATHER_API_KEY": "your_api_key_here"
      }
    }
  }
}

To enable debug logging, add the DEBUG environment variable:

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/weather-mcp-server",
        "run",
        "server.py"
      ],
      "env": {
        "WEATHER_API_KEY": "your_api_key_here",
        "DEBUG": "true"
      }
    }
  }
}

Usage

Running the Server

Direct execution:

WEATHER_API_KEY=your_api_key uv run server.py

With debug logging:

DEBUG=true WEATHER_API_KEY=your_api_key uv run server.py

With MCP client: The server automatically starts when your MCP client (like mcphost or Claude Desktop) connects to it.

Available Tools

1. weather_current

Get current weather conditions for a location.

Parameters:

  • q (required): Location query (city name, coordinates, postal code)

  • aqi (optional): Include air quality data ("yes" or "no", default: "no")

Example:

{
  "name": "weather_current",
  "arguments": {
    "q": "New York, NY",
    "aqi": "yes"
  }
}

2. weather_forecast

Get weather forecast for 1-14 days.

Parameters:

  • q (required): Location query

  • days (optional): Number of forecast days (1-14, default: 1)

Example:

{
  "name": "weather_forecast",
  "arguments": {
    "q": "London, UK",
    "days": 7
  }
}

3. weather_search

Search for locations matching a query.

Parameters:

  • q (required): Search query

Example:

{
  "name": "weather_search",
  "arguments": {
    "q": "Paris"
  }
}

Location Query Formats

The weather tools accept various location formats:

  • City name: "New York", "London"

  • City and state/country: "New York, NY", "London, UK"

  • Coordinates: "40.7128,-74.0060"

  • Postal code: "10001", "SW1A 1AA"

  • Airport code: "JFK", "LHR"

Testing

Manual Testing

Test the server with JSON-RPC requests:

echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | WEATHER_API_KEY=your_api_key uv run server.py

With debug logging:

echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | DEBUG=true WEATHER_API_KEY=your_api_key uv run server.py

Tool Testing

Test a weather tool:

echo '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"weather_current","arguments":{"q":"New York"}}}' | WEATHER_API_KEY=your_api_key uv run server.py

Docker Support

Building the Image

docker build -t weather-mcp-server .

Running with Docker

docker run --rm -i -e WEATHER_API_KEY=your_api_key weather-mcp-server

Debug Mode

The server includes optional debug logging. Set the DEBUG environment variable to enable detailed logging.

Log levels:

  • INFO: Basic server operations (default)

  • DEBUG: Detailed request/response information, API calls (when DEBUG=true)

Error Handling

The server handles common errors gracefully:

  • Invalid API key: Returns error message with guidance

  • Location not found: Suggests alternative search terms

  • Network issues: Provides retry suggestions

  • Rate limiting: Indicates when limits are exceeded

Dependencies

  • httpx: HTTP client for API requests

  • asyncio: Async/await support

  • Standard library: json, logging, sys, os

API Limits

WeatherAPI free tier includes:

  • 1 million calls per month

  • Current weather and 3-day forecast

  • Upgrade for extended forecasts and higher limits

Troubleshooting

Common Issues

  1. "No content response received"

    • Check API key is set correctly

    • Verify network connectivity

    • Review debug logs for errors

  2. "API key not provided"

    • Set the WEATHER_API_KEY environment variable

    • Check the MCP client configuration

  3. "Location not found"

    • Try different location formats

    • Use the weather_search tool to find valid locations

Debug Output

Enable detailed logging by setting the DEBUG environment variable:

DEBUG=true WEATHER_API_KEY=your_api_key uv run server.py 2>&1 | grep -E "(DEBUG|ERROR)"

Architecture

This server implements the MCP protocol using:

  • stdio communication: Direct JSON-RPC over stdin/stdout

  • Asyncio event loop: Non-blocking request handling

  • Manual JSON-RPC: Custom implementation for precise control

  • WeatherAPI integration: RESTful API calls with error handling

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Test thoroughly

  5. Submit a pull request

License

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

Support

Last updated: July 12, 2025

Available Tools

3 tools
weather_currentC

Get current weather for a location

ParametersJSON Schema
NameRequiredDescriptionDefault
qYesLocation query (city name, lat/lon, postal code, etc)
aqiNoInclude air quality data ('yes' or 'no')no

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 the tool's function but does not cover important aspects such as rate limits, authentication needs, error handling, or response format. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted language, making it efficient and easy to parse for an AI agent.

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. It does not address behavioral traits, response format, or usage context, which are crucial for a tool with parameters and no structured output information. This leaves the agent with insufficient information to fully understand the tool's operation.

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 input schema has 100% description coverage, clearly documenting both parameters ('q' for location query and 'aqi' for air quality data). The description does not add any additional meaning beyond what the schema provides, so it meets the baseline score of 3 for adequate but not enhanced parameter semantics.

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 with a specific verb ('Get') and resource ('current weather for a location'), making it easy to understand what it does. However, it does not explicitly differentiate itself from sibling tools like 'weather_forecast' or 'weather_search', which might offer similar weather-related functionality.

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 like 'weather_forecast' or 'weather_search'. It lacks context about use cases, exclusions, or prerequisites, leaving the agent to infer usage based on the tool name alone.

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

weather_forecastC

Get weather forecast (1-14 days) for a location

ParametersJSON Schema
NameRequiredDescriptionDefault
qYesLocation query (city name, lat/lon, postal code, etc)
daysNoNumber of days (1-14)

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 lacks critical behavioral details such as rate limits, authentication requirements, error handling, or response format. For a tool with no 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.

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 functionality ('Get weather forecast') and includes key constraints (1-14 days, for a location) without any wasted words. It's appropriately sized for the tool's complexity.

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. It doesn't address behavioral aspects like rate limits or auth, and while the input schema is well-documented, the description fails to compensate for missing context about what the forecast returns (e.g., temperature, precipitation) or how errors are handled.

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%, so the input schema already fully documents both parameters (q and days). The description adds minimal value beyond the schema by implying the tool uses location queries and a day range, but doesn't provide additional syntax, format details, or usage examples. This meets the baseline for high schema coverage.

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 with a specific verb ('Get') and resource ('weather forecast'), and includes the time range (1-14 days) and target ('for a location'). However, it doesn't explicitly differentiate from its sibling tools (weather_current and weather_search), which would require mentioning it provides future predictions rather than current conditions or search functionality.

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 siblings (weather_current and weather_search). It doesn't mention alternatives, exclusions, or specific contexts for use, leaving the agent to infer 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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updates
    • First observedweather_current
    • First observedweather_forecast
    • First observedweather_search
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: weather_current retrieves current conditions, weather_forecast provides future predictions, and weather_search finds locations. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tools follow a consistent 'weather_' prefix pattern with descriptive suffixes (current, forecast, search). This uniform naming convention makes the tool set predictable and easy to understand.

Tool Count5/5

With 3 tools, the server is well-scoped for a weather service, covering core needs: current conditions, forecasts, and location lookup. Each tool earns its place without being excessive or insufficient.

Completeness4/5

The tool set covers essential weather operations for a typical agent, including current data, forecasts, and location search. A minor gap might be historical weather data, but core workflows are well-supported without dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • MCP Weather Server

    le-yo
    A
    license
    B
    quality
    D
    maintenance
    A simple server that implements the Model Context Protocol, allowing AI models like Claude to fetch real-time weather information for any location using the wttr.in API.
    4
    22
    1
    MIT

  • Weather MCP Server

    im-zhong
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables users to fetch weather forecasts through Cursor AI by using natural language queries.
    MIT

  • MCP Weather Server

    microsoft
    A
    license
    B
    quality
    D
    maintenance
    A simple Model Context Protocol server that provides real-time weather data to AI agents like GitHub Copilot, allowing users to get current weather information for any city through natural language queries.
    2
    32
    97
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/first-it-consulting/weather-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server