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by yang648557392
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Wetter-Tool-Calling mit Cline und MCP

Ein kleiner Python-MCP-Server, der einem LLM das Abrufen echter Wetterdaten ermöglicht.

Der Nutzer stellt in Cline eine Wetterfrage. Das LLM entscheidet, ob ein Wetter-Tool aufgerufen werden soll, generiert die erforderlichen Argumente und sendet die Anfrage an diesen Server. Der Server ruft Daten vom US National Weather Service (NWS) ab. Anschließend verwendet das LLM das Ergebnis, um die endgültige Antwort zu formulieren.

So funktioniert es

User question
    -> Cline sends the question and tool definitions to the LLM
    -> The LLM selects a weather tool and generates its arguments
    -> Cline calls the Python MCP server
    -> The server fetches real data from api.weather.gov
    -> The LLM turns the tool result into a natural-language answer

Related MCP server: Weather MCP Server

Technologie-Stack

  • Python 3.14

  • MCP Python SDK

  • Cline als MCP-Client und LLM-Host

  • OpenRouter mit einem DeepSeek-Modell für die Demo

  • HTTPX

  • US National Weather Service API

  • uv

Tools

get_forecast

Ruft die nächsten fünf Vorhersagezeiträume für einen US-Standort ab.

{
  "latitude": 40.7128,
  "longitude": -74.006
}

get_alerts

Ruft aktive Wetterwarnungen für einen US-Bundesstaat ab.

{
  "state": "NY"
}

Einrichtung

Voraussetzungen:

  • Python 3.14

  • uv

  • Visual Studio Code mit Cline

  • Ein in Cline konfigurierter API-Schlüssel für ein LLM mit Tool-Unterstützung.

Installieren Sie das Projekt:

git clone <https://github.com/yang648557392/weather-mcp-tool-calling.git>
cd weather
uv sync

Fügen Sie den MCP-Server zu den MCP-Einstellungen von Cline hinzu. Ersetzen Sie den Pfad durch den absoluten Pfad zu diesem Projekt:

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

Starten Sie den MCP-Server in Cline neu. Cline sollte get_forecast und get_alerts automatisch erkennen.

Wenn Cline uv nicht finden kann, ersetzen Sie "uv" durch den absoluten Pfad, der von Folgendem zurückgegeben wird:

which uv

Verwendung

Stellen Sie Cline eine Frage, zum Beispiel:

What will the weather be like in New York tomorrow?
Are there any active weather alerts in California?

Cline zeigt das ausgewählte Tool, seine Argumente, das Tool-Ergebnis und die endgültige Antwort des LLM an.

Einschränkungen

  • Die NWS-API unterstützt nur Standorte, die vom US-Wetterdienst abgedeckt werden.

  • Das LLM ist dafür verantwortlich, einen Ortsnamen in Koordinaten umzuwandeln.

  • Cline wird als LLM-Host und MCP-Client benötigt.

  • Provider-API-Schlüssel werden in Cline gespeichert und dürfen nicht in dieses Repository committet werden.

Autor

Mingzhe Yang

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

B3.2/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are clearly distinct: get_alerts retrieves weather warnings for a state, while get_forecast retrieves forecast data by coordinates. There is no overlap in their inputs or outcomes.

Naming Consistency5/5

Both tools follow the same 'get_noun' pattern, with get_alerts and get_forecast. The naming is predictable and consistent.

Tool Count3/5

With only two tools, the server is minimal and borderline scoped. While this could be fine for a specialized alerts/forecast service, it feels thin for a general weather service and does not reach the 3-15 tool sweet spot.

Completeness2/5

A weather service would typically include current conditions, hourly/daily details, or location-based lookup beyond forecast and alerts. The absence of these leaves significant gaps for users expecting general weather coverage.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

    gaurigshankar
    A
    license
    B
    quality
    D
    maintenance
    Enables AI assistants to access real-time US weather forecasts and alerts through the National Weather Service API.
    2
    9 npm
    MIT

  • Weather MCP Server

    aitiwari
    F
    license
    B
    quality
    D
    maintenance
    Provides real-time US weather alerts and forecasts by integrating with the National Weather Service API. It enables AI assistants to fetch state-specific alerts and detailed local forecasts using geographic coordinates.
    2
    1
    -

  • mcp-weather-api

    devgrunge
    F
    license
    Not graded
    quality
    D
    maintenance
    Provides weather forecasts and alerts for US locations via the National Weather Service API, enabling AI assistants to deliver real-time weather information.
    16 npm
    -