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karannsharma01

Weather MCP Server

by karannsharma01
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Python
Remote

Weather MCP Server

A Model Context Protocol (MCP) server that provides weather information for the United States using the National Weather Service (NWS) API. Built with FastMCP.

Features

This server exposes tools that allow AI assistants to fetch real-time weather data:

  • get_alerts(state: str): Get active weather alerts for a given US state (using the 2-letter state code like CA, NY, TX). Returns details on the event, severity, description, and safety instructions.

  • get_forecast(latitude: float, longitude: float): Get the detailed weather forecast for a specific location (using latitude and longitude). Returns the forecast for the next 5 periods (e.g., this afternoon, tonight, tomorrow).

Related MCP server: weather-mcp

Prerequisites

  • Python 3.10 or higher

  • uv package manager

Installation & Setup

This project uses uv for dependency management. To set it up:

  1. Clone the repository

  2. Install dependencies (this will create a virtual environment if you run it using uv):

    uv sync

Usage

You can run the MCP server manually via standard input/output, which is how MCP clients will interact with it:

uv run weather.py

Integrating with Claude Desktop

To use this server with Claude Desktop, you would add it to your claude_desktop_config.json:

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

Integrating with a custom MCP Client

If you have a custom Python MCP Client, you can connect to it like this:

from mcp import StdioServerParameters

server_params = StdioServerParameters(
    command="uv",
    args=["--directory", "path/to/mcp-server", "run", "weather.py"],
    env=None
)
# Pass server_params to your MCP client

APIs Used

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, the description carries full burden for behavioral traits. It only states 'Get weather alerts', implying a read operation, but fails to disclose any additional behaviors such as data freshness, scope limitations, or whether it returns current alerts only.

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 two short sentences, front-loading the purpose and parameter format. Every sentence adds value without redundancy.

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 simplicity of the tool (one parameter, output schema exists), the description is adequate but omits useful context such as the data source, update frequency, or typical response structure. It meets minimum viability but has room for improvement.

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 description adds the format clue 'Two-letter US state code (e.g. CA, NY)' for the state parameter, which is not in the schema. However, schema coverage is 0%, so more detail (e.g., case sensitivity, accepted values) would have been beneficial.

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 that the tool gets weather alerts for a US state, specifying the resource and scope. However, it does not explicitly differentiate it from the sibling tool get_forecast, though the resource type (alerts vs forecast) is distinct.

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 on when to use this tool versus the sibling tool get_forecast. The description simply states what it does without any context on appropriate use cases or alternatives.

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 longitude: Longitude of the location

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states it gets a forecast (implying a read operation) but does not mention any additional behavioral traits such as data sources, freshness, limitations, or error conditions.

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 very short, with two sentences. It is front-loaded with the main purpose, but the 'Args:' section is slightly verbose and could be more concise. Overall, it is 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?

For a simple tool with two parameters and an output schema, the description covers the basic purpose and parameter meanings. However, it lacks usage guidance and behavioral context, making it just adequate for the complexity level.

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?

Although the input schema has no descriptions (0% coverage), the description provides a brief explanation for each parameter: 'Latitude of the location' and 'Longitude of the location.' This adds meaning beyond the schema types, though it is minimal.

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 clearly states the tool's purpose: 'Get weather forecast for a location.' It uses a specific verb ('get') and resource ('weather forecast'), and it implicitly distinguishes from the sibling tool 'get_alerts' by focusing on forecasts rather than alerts.

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 use this tool versus the sibling 'get_alerts' or any other tool. No prerequisites or context are provided, making it difficult for an agent to decide to use this over alternatives.

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

B3.4/5.0
Disambiguation5/5

get_alerts and get_forecast have clearly distinct purposes: one for weather alerts by state, the other for forecast by coordinates. No overlap exists.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (get_alerts, get_forecast), making them predictable.

Tool Count3/5

With only 2 tools, the server feels thin for a weather domain, bordering on insufficient scope.

Completeness2/5

Common weather operations like current conditions, hourly forecast, or radar are missing, leaving significant gaps for typical use cases.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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