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Weather MCP Server

by karannsharma01
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Wetter-MCP-Server

Ein Model Context Protocol (MCP)-Server, der Wetterinformationen für die Vereinigten Staaten über die API des National Weather Service (NWS) bereitstellt. Erstellt mit FastMCP.

Funktionen

Dieser Server stellt Tools bereit, mit denen KI-Assistenten Wetterdaten in Echtzeit abrufen können:

  • get_alerts(state: str): Ruft aktive Wetterwarnungen für einen bestimmten US-Bundesstaat ab (unter Verwendung des 2-stelligen Bundesstaat-Codes wie CA, NY, TX). Gibt Details zum Ereignis, zum Schweregrad, eine Beschreibung und Sicherheitshinweise zurück.

  • get_forecast(latitude: float, longitude: float): Ruft die detaillierte Wettervorhersage für einen bestimmten Standort ab (unter Verwendung von Breitengrad und Längengrad). Gibt die Vorhersage für die nächsten 5 Zeiträume zurück (z. B. heute Nachmittag, heute Abend, morgen).

Related MCP server: weather-mcp

Voraussetzungen

  • Python 3.10 oder höher

  • uv Paketmanager

Installation & Einrichtung

Dieses Projekt verwendet uv für das Abhängigkeitsmanagement. Zur Einrichtung:

  1. Klonen Sie das Repository

  2. Installieren Sie die Abhängigkeiten (dies erstellt eine virtuelle Umgebung, wenn Sie es mit uv ausführen):

    uv sync

Verwendung

Sie können den MCP-Server manuell über die Standard-Ein-/Ausgabe ausführen, so wie MCP-Clients mit ihm interagieren werden:

uv run weather.py

Integration mit Claude Desktop

Um diesen Server mit Claude Desktop zu verwenden, fügen Sie ihn Ihrer claude_desktop_config.json hinzu:

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

Integration mit einem benutzerdefinierten MCP-Client

Wenn Sie einen benutzerdefinierten Python-MCP-Client haben, können Sie sich wie folgt damit verbinden:

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

Verwendete APIs

Available Tools

2 tools
get_alertsA

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

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_forecastC

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

C2.8/5.0
Behavior2/5

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

There are no annotations, so the description must carry behavioral context. It only says 'get' a forecast and gives no indication of units, time range, coordinate format, or whether this is a read-only operation. Nothing contradicts annotations, but little is disclosed beyond the basic action.

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 the core purpose is front-loaded. The Args block is somewhat redundant with the schema but does not add significant bloat, keeping the overall entry compact.

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?

The description provides the essential call requirements (latitude and longitude) and the presence of an output schema reduces the need to document return values. However, it omits practical context like expected coordinate units, available forecast periods, and why an agent would choose this over get_alerts.

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 Args section repeats the parameter names with minimal glosses ('Latitude of the location'), adding almost no meaning beyond the schema titles. Since schema description coverage is 0%, the description should compensate with coordinate format or range details, but it does not.

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 uses a clear verb and resource: 'Get weather forecast for a location'. It does not explicitly mention the sibling get_alerts, but the forecast-vs-alerts distinction is clear enough from the domain.

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 given about when to use this tool instead of get_alerts, nor are any exclusions or alternative conditions provided. The intended usage is only implied by the tool name and description.

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.

  1. 2 tool updatesv0.1.0
    • First observedget_alerts
    • First observedget_forecast
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored across 2 tools

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
ResponsivenessNo issues

Resources

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