A
licenseNot graded
qualityD
maintenanceProvides access to National Weather Service (NWS) data, enabling users to retrieve active weather alerts for US states and weather forecasts for specific geographic coordinates.
85 npm
MIT
Weather MCP Server
by OleBo
Click on "Deploy 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 Serverwhat's the forecast for New York City?"
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.
Weather MCP Server
A Model Context Protocol (MCP) server that provides weather information and alerts from the National Weather Service (NWS).
Features
get_alerts: Retrieve active weather alerts for a US state
get_forecast: Get weather forecasts for a specific location (latitude/longitude)
Related MCP server: Weather MCP Server
Prerequisites
Python 3.10 or higher
Docker and Docker Compose (for containerized deployment)
Installation
Local Setup
# Clone the repository
git clone https://github.com/yourusername/weather-mcp.git
cd weather-mcp
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -e .
# Run the server
weather-mcpDocker Setup
# Build and run with Docker Compose
docker-compose up -d
# View logs
docker-compose logs -f weather-mcpConfiguration
Claude Desktop Integration
To use this server with Claude Desktop, add the following to ~/.config/Claude/claude_desktop_config.json (Linux/macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
Option 1: Local Installation
{
"mcpServers": {
"weather": {
"command": "uv",
"args": [
"--directory",
"/path/to/weather-mcp",
"run",
"-m",
"weather.main"
]
}
}
}Option 2: Docker Container
{
"mcpServers": {
"weather": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"weather-mcp-server"
]
}
}
}Development
Running Tests
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Run tests with coverage
pytest --cov=src/weatherCode Formatting and Linting
# Format code with Black
black src tests
# Lint with Ruff
ruff check src testsProject Structure
weather-mcp/
├── src/weather/ # Main package
│ ├── __init__.py
│ ├── main.py # Entry point
│ └── server.py # Server implementation
├── config/ # Configuration files
│ ├── __init__.py
│ └── settings.py
├── tests/ # Test suite
├── Dockerfile # Docker image definition
├── docker-compose.yml # Docker Compose configuration
├── pyproject.toml # Project metadata and dependencies
├── README.md # This file
└── DOCKER_SETUP.md # Docker setup guideAPI Reference
get_alerts(state: str) -> str
Get active weather alerts for a US state.
Parameters:
state: Two-letter US state code (e.g., CA, NY, TX)
Returns: Formatted string containing active weather alerts or a message if none found.
get_forecast(latitude: float, longitude: float) -> str
Get weather forecast for a specific location.
Parameters:
latitude: Location latitude (up to 4 decimal places recommended)longitude: Location longitude (up to 4 decimal places recommended)
Returns: Formatted string with the next 5 forecast periods including temperature, wind, and conditions.
Troubleshooting
Server won't start
Check Python version:
python --version(requires 3.10+)Ensure dependencies are installed:
pip install -e .Check logs for detailed error messages
Docker issues
Rebuild image:
docker-compose build --no-cacheView logs:
docker-compose logs weather-mcpEnsure Docker daemon is running
Claude Desktop not detecting server
Verify the path in
claude_desktop_config.jsonRestart Claude Desktop after configuration changes
Check the MCP server settings in Claude Desktop
Logging
The server logs to stderr to avoid interfering with the MCP protocol's use of stdout. Logs are automatically sent to stderr and include timestamps and severity levels.
License
MIT License - see LICENSE file for details
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Support
For issues, questions, or suggestions, please open an issue on GitHub.
Available Tools
2 toolsget_alertsA
Get weather alerts for a US state.
Args: state: Two-letter US state code (e.g. CA, NY)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. 'Get' implies a read-only operation, but the description does not explicitly state side-effect-free behavior or any caveats about alert types or data source. It is not misleading, but it adds minimal behavioral context beyond what the name implies.
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, with a front-loaded purpose statement followed by a compact Args block. Every sentence earns its place and there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a single well-documented parameter, an output schema, and no siblings, the description plus schema fully covers what an agent needs to invoke the tool correctly. No missing context for this simple operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description fully compensates by specifying the parameter format ('Two-letter US state code') and providing concrete examples ('CA, NY'). This adds real meaning beyond the raw schema type string.
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 states a specific verb ('Get'), a clear resource ('weather alerts'), and a clear scope ('US state'). It is unambiguous and leaves no doubt about what the tool does, even without siblings to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There are no sibling tools, so explicit routing guidance is unnecessary. The description clearly implies usage: when you need weather alerts for a US state. It lacks explicit exclusions, but nothing is misleading or missing for a tool of this simplicity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_forecastB
Get weather forecast for a location.
Args: latitude: Latitude of the location (recommended: up to 4 decimal places) longitude: Longitude of the location (recommended: up to 4 decimal places)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
B3.3/5.0
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 only states what the tool does ('Get weather forecast') without mentioning any behavioral traits like rate limits, authentication requirements, data freshness, error conditions, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps for an agent to understand how to use it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence. The parameter details are structured clearly under 'Args:' but could be slightly more concise by integrating recommendations into a single line per parameter. Overall, it's efficient with minimal waste.
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 required parameters) and the presence of an output schema (which handles return values), the description is partially complete. It covers the purpose and parameters well but lacks behavioral context and usage guidelines. With no annotations, it should provide more operational details to be fully helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explicitly lists both parameters (latitude and longitude) and provides practical guidance: 'recommended: up to 4 decimal places.' This clarifies precision expectations that aren't captured in the schema's basic type definitions, fully compensating for the schema's lack of descriptions.
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: 'Get weather forecast for a location.' This specifies both the verb ('Get') and the resource ('weather forecast') with the target ('location'). However, it doesn't explicitly differentiate from the sibling tool 'get_alerts', which likely provides different weather-related information.
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. There's no mention of the sibling tool 'get_alerts', nor any context about when a forecast is appropriate versus alerts. Usage is implied through the tool name but not explicitly 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.
2 tool updates
- First observed
get_alerts - First observed
get_forecast
TDQS
B3.3/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: get_alerts retrieves weather alerts for a US state, while get_forecast provides weather forecasts for a specific geographic location. There is no overlap in functionality or ambiguity between them.
Naming Consistency5/5
Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. This makes the tool set predictable and easy to understand.
Tool Count2/5
With only two tools, this server feels under-scoped for a weather domain. While the tools cover alerts and forecasts, there are obvious missing capabilities like current conditions, historical data, or radar imagery that would be expected from a weather service.
Completeness2/5
The tool surface is severely incomplete for a weather server. It lacks fundamental operations such as getting current weather conditions, accessing radar or satellite data, or retrieving historical weather information. Agents will face significant limitations when trying to perform comprehensive weather-related tasks.
Maintenance
ActivityInactive
ResponsivenessNo issues
Related MCP Connectors
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