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AmplifyAutomation

Weather MCP Server

by AmplifyAutomation
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Python
Remote

Weather MCP Server

A Model Context Protocol (MCP) server that provides weather data from the US National Weather Service (NWS) API. Use this server to get weather forecasts and active weather alerts for US locations directly within Claude.

Features

This server exposes two tools:

  • get_forecast - Get detailed weather forecasts for any US location by latitude/longitude

  • get_alerts - Get active weather alerts for any US state

Related MCP server: weather-mcp

Prerequisites

  • Python 3.13+

  • uv package manager

Installation

  1. Clone the repository:

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

  2. Install dependencies:

    uv sync

Configuration

Claude Desktop

Add the following to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

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

Replace /path/to/weather-mcp-server with the actual path to this project.

Claude Code

Add to your Claude Code settings or project's .mcp.json:

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

Usage Examples

Once configured, you can ask Claude:

  • "What's the weather forecast for San Francisco?" (Claude will look up coordinates)

  • "Get the forecast for latitude 37.7749, longitude -122.4194"

  • "Are there any weather alerts in California?"

  • "What weather warnings are active in TX?"

Tools Reference

get_forecast

Get weather forecast for a specific location.

Parameters:

  • latitude (float): Latitude of the location

  • longitude (float): Longitude of the location

Returns: Detailed forecast for the next 5 periods including temperature, wind, and conditions.

get_alerts

Get active weather alerts for a US state.

Parameters:

  • state (string): Two-letter US state code (e.g., CA, NY, TX)

Returns: List of active alerts including event type, affected area, severity, description, and instructions.

Data Source

Weather data is provided by the National Weather Service API, which is free to use and does not require an API key. Note that this API only covers US locations.

License

MIT

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

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral disclosure burden, but it only restates the core purpose of retrieving alerts. It does not mention return format, data source, update frequency, or whether this is a safe read-only operation. This is a minimal behavioral statement rather than useful 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 compact, front-loaded, and every sentence earns a place: the first line states the action, the second documents the only parameter. There is no filler or redundant information.

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?

For a tool with one simple parameter, the description is near-adequate. However, with no output schema and no annotations, it does not describe what the returned alerts contain or how to distinguish this tool from get_forecast when selecting. A small but real completion gap remains.

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 input schema only defines state as a string, so the description's 'Two-letter US state code (e.g. CA, NY)' adds important format and example context. It fully clarifies the sole parameter, though it does not enumerate all valid state codes or explain invalid input behavior.

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 opens with a specific verb and object ('Get weather alerts for a US state'), clearly identifying the tool's function. It does not explicitly contrast with sibling get_forecast, but the word 'alerts' already separates it from a forecast tool. Clear, but stops short of explicit sibling differentiation.

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?

There is no guidance on when to choose this tool over get_forecast or what conditions call for alerts versus forecast. The only usage-related content is the state parameter, which belongs more to parameter semantics. The agent receives no decision support for tool selection.

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

get_forecastC

Get weather forecast for a location.

Args: latitude: Latitude of the location longitude: Longitude of the location

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description bears the full burden of behavioral disclosure. It only says 'Get weather forecast' and gives no information about safety (read-only vs. side effects), units, time range, caching, or output structure. Such missing details matter for an external forecast API.

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 short and purpose-first, with a separate Args block for parameters. It is efficiently structured, though the Args section largely duplicates the input schema and could be trimmed without loss.

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?

With no output schema and no annotations, the description should explain what forecast data is returned (e.g., temperature, precipitation, time horizon), coordinate constraints, and how this relates to the sibling get_alerts. It does none of these, so an agent lacks essential information for correct invocation and interpretation.

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

Parameters2/5

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

The description repeats the parameter names with minimal elaboration ('Latitude of the location'), which adds little beyond the input schema titles 'Latitude' and 'Longitude'. It does not specify units (e.g., decimal degrees), valid ranges, or coordinate format, so the parameter semantics are under-specified.

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 states a clear verb and resource: 'Get weather forecast for a location.' This distinguishes it from the sibling tool get_alerts, which obviously deals with alerts rather than forecasts, even though it does not explicitly name the sibling.

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?

No guidance is provided about when to use get_forecast versus get_alerts or any other alternative. The description gives no contextual hints about use cases, prerequisites, or exclusions.

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 updatesv0.1.0
    • First observedget_alerts
    • First observedget_forecast
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TDQS

B3.2/5.0
Disambiguation5/5

get_alerts and get_forecast are clearly distinct in both purpose and input parameters—one targets alerts by state, the other forecasts by coordinates. There is no overlap or ambiguity between the two.

Naming Consistency5/5

Both tools follow the same get_ verb_noun pattern, making the naming scheme predictable and consistent. No mixed conventions or vague verbs.

Tool Count3/5

With only two tools, the server sits at the thin end of the spectrum. Each tool is useful, but the count feels minimal for a weather service that could reasonably offer current conditions, hourly forecasts, or location search.

Completeness3/5

The core alert and forecast capabilities are covered, but obvious weather-domain operations are missing, such as current conditions, location search, or more granular forecast types. Agents may need to work around these gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

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