The Weather MCP Server retrieves real-time weather information for specified cities using the OpenWeatherMap API.
Fetch Weather Data: Get detailed weather reports including temperature, humidity, wind speed, sunrise/sunset times, and weather descriptions
Metric Units: Weather data is provided in metric units (e.g., Celsius for temperature)
Integration: Can be configured to work with Claude Desktop for weather data retrieval
Developer-Friendly: Supports manual installation and configuration for development environments
Uses .env files for configuration management, particularly for storing the OpenWeatherMap API key
Includes instructions for cloning the repository as part of the setup process
Offers specific setup instructions for Linux environments
Includes macOS-specific setup instructions for the virtual environment
Requires Python 3.12 or higher for running the server
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., "@Weather MCP Server what's the weather like in London today?"
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.
A Modern Code Protocol (MCP) server that provides weather information using the OpenWeatherMap API.
Real-time weather data retrieval
Metric units for temperature
Detailed weather information including:
Temperature
Humidity
Wind Speed
Sunrise/Sunset times
Weather description
Related MCP server: Open Weather13 MCP Server
Python 3.12 or higher
OpenWeatherMap API key
To install Weather MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @CodeByWaqas/weather-mcp-server --client claudeClone the repository
Create a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows use: .venv\Scripts\activateInstall dependencies:
pip install -e .# claude_desktop_config.json
# Can find location through:
# Claude -> Settings -> Developer -> Edit Config
{
"mcpServers": {
"mcp-weather-project": {
"command": "uv",
"args": [
"--directory",
"/<absolute-path>/weather-mcp-server/src/resources",
"run",
"server.py"
],
"env": {
"WEATHER_API_KEY": "YOUR_API_KEY"
}
}
}
}git clone https://github.com/CodeByWaqas/weather-mcp-server
Install MCP server dependencies:
cd weather-mcp-server
# Create virtual environment and activate it
uv venv
source .venv/bin/activate # MacOS/Linux
# OR
.venv/Scripts/activate # Windows
# Install dependencies
uv add "mcp[cli]" python-dotenv requests httpxCopy src/resources/env.example to src/resources/.env
Add your OpenWeatherMap API key to the .env file:
WEATHER_API_KEY=your_api_key_hereRun the Claude Desktop and use LLM to retrieve weather info
This project is licensed under the MIT License - see the LICENSE file for details.
It fetches the latest weather reports for the given city. Args: city (str): The city name for which weather reports are required. Returns: dict: The weather reports for the given city.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
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 states the tool fetches 'latest' reports, implying real-time or recent data, but doesn't disclose behavioral traits like rate limits, error handling, data sources, or whether it's read-only. For a tool with no annotations, this leaves significant gaps in understanding its 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 appropriately sized and front-loaded, with the purpose stated first. The Args and Returns sections are structured but could be more integrated. It avoids unnecessary details, though the formatting with quotes and line breaks is slightly awkward.
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 no annotations, no output schema, and low parameter semantics coverage, the description is incomplete. It lacks information on return format details (beyond 'dict'), error cases, or operational constraints. For a tool fetching external data, this leaves the agent with insufficient context to use it effectively.
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 description adds minimal semantics beyond the input schema. It explains that 'city' is 'The city name for which weather reports are required,' which clarifies the parameter's purpose but doesn't provide format details (e.g., city name conventions) or examples. With 0% schema description coverage and 1 parameter, the baseline is 4, but the description only partially compensates, so a 3 is appropriate.
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 tool's purpose: 'fetches the latest weather reports for the given city.' It specifies the verb ('fetches') and resource ('weather reports'), though it doesn't need to distinguish from siblings since none exist. The purpose is specific and unambiguous.
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. It mentions the city parameter but offers no context about prerequisites, limitations, or typical use cases. With no siblings, this is less critical, but the description still lacks usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
weatherWith only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear, distinct purpose of fetching weather reports for a city.
The single tool name 'weather' is straightforward and descriptive. Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.
A single tool for a weather server is too few for the apparent scope, as it lacks essential operations like forecasts, historical data, or multi-location queries. This minimal set limits functionality and may cause agent failures in broader weather-related tasks.
The tool surface is severely incomplete for a weather domain, missing obvious gaps such as forecasts, alerts, or location-based searches. While it covers basic current weather, agents will struggle with common weather-related workflows due to these significant omissions.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
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