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OleBo

Weather MCP Server

by OleBo
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

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-mcp

Docker Setup

# Build and run with Docker Compose
docker-compose up -d

# View logs
docker-compose logs -f weather-mcp

Configuration

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/weather

Code Formatting and Linting

# Format code with Black
black src tests

# Lint with Ruff
ruff check src tests

Project 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 guide

API 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-cache

  • View logs: docker-compose logs weather-mcp

  • Ensure Docker daemon is running

Claude Desktop not detecting server

  • Verify the path in claude_desktop_config.json

  • Restart 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 tools
get_alertsB

Get weather alerts for a US state.

Args: state: Two-letter US state code (e.g. CA, NY)

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.1/5.0
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 what the tool does but doesn't describe any behavioral traits: no information about rate limits, authentication requirements, response format, error handling, or whether this is a read-only operation. The description is purely functional without behavioral context.

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 concise with two sentences: one stating the purpose and one explaining the parameter. It's front-loaded with the main purpose. The structure is clear, though the 'Args:' section formatting is slightly informal for MCP standards.

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 has an output schema (which handles return values), the description covers the basic purpose and parameter semantics adequately. However, for a tool with no annotations and a sibling tool available, it should provide more context about when to use it versus alternatives and more behavioral information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant value beyond the input schema, which has 0% description coverage. While the schema only shows a 'state' parameter with type 'string', the description specifies it must be a 'Two-letter US state code' and provides examples ('e.g. CA, NY'). This clarifies the expected format that isn't evident from the schema alone.

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 alerts for a US state.' It specifies the verb ('Get'), resource ('weather alerts'), and geographic scope ('US state'). However, it doesn't explicitly differentiate from its sibling tool 'get_forecast', which likely provides different weather 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. It doesn't mention the sibling tool 'get_forecast' or explain what distinguishes weather alerts from forecasts. There's no context about when alerts are needed versus general forecasts.

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
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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. Dates show when Glama detected each change.

  1. 2 tool updates
    • First observedget_alerts
    • First observedget_forecast
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TDQS

B3.3/5.0
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

Resources

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Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

    BhavinPatel04
    A
    license
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    Provides access to National Weather Service (NWS) data, enabling users to retrieve active weather alerts for US states and weather forecasts for specific geographic coordinates.
    36
    MIT

  • Weather MCP Server

    x8yuv
    A
    license
    Not graded
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    Provides weather forecasts and alerts for US locations using the National Weather Service API. Supports getting detailed forecasts by coordinates and active weather alerts by state code.
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  • MCP Weather Server

    xue20010808
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    license
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    maintenance
    Provides weather information using the US National Weather Service API, including active weather alerts for US states and location-specific forecasts based on latitude and longitude coordinates.
    36
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