MCP Weather Agent
🌤️ MCP Wetter-Agent
Ein intelligenter, multi-regionaler Wetterassistent, unterstützt von OpenAI und dem Model Context Protocol (MCP).
Erhalten Sie Echtzeit-Wettervorhersagen und -warnungen für Israel und die Vereinigten Staaten über eine natürlichsprachliche Chat-Oberfläche. Der Agent leitet Fragen intelligent an die passende Datenquelle weiter und erstellt Antworten aus Live-Webdaten und offiziellen Wetter-APIs.
✨ Funktionen
🤖 KI-gestützter Chat: Konversationelle Wetterabfragen mit GPT-4o-mini
🌍 Zwei Datenquellen:
🇮🇱 Israel: Live-Web-Scraping von Weather2day über Playwright
🇺🇸 USA: Offizielle National Weather Service API
🔄 Intelligentes Tool-Routing: Der Agent wählt automatisch das richtige Tool basierend auf Ihrer Frage
💬 Natürliche Sprache: Stellen Sie Fragen in einfachem Englisch; der Agent versteht Kontext und Absicht
🔐 Sicher: API-Schlüssel werden in
.envgespeichert und von der Versionskontrolle ausgeschlossen🎯 Interaktiv: Echtzeit-Chat-Schleife mit sichtbarer Browser-Automatisierung für Israel-Abfragen
Related MCP server: MCP Weather Assistant
📋 Inhaltsverzeichnis
🚀 Schnellstart
In 3 Minuten einsatzbereit:
# 1. Install dependencies
uv sync
uv run playwright install chromium
# 2. Create .env file with your OpenAI API key
copy .env.example .env
# Edit .env and add: OPENAI_API_KEY=sk-...
# 3. Run the agent
uv run .\host.py
# 4. Ask a weather question!
# Example: "What is the weather forecast for Jerusalem?"📦 Anforderungen
Komponente | Version | Zweck |
Python | 3.13+ | Laufzeitumgebung |
OpenAI API-Schlüssel | — | GPT-4o-mini-Modellzugriff |
uv | Aktuell | Schneller Python-Paketmanager |
Chromium | Aktuell | Browser-Automatisierung (Playwright) |
Internet | — | API-Zugriff und Web-Scraping |
🔧 Einrichtung
1. Abhängigkeiten installieren
Öffnen Sie ein Terminal im Projektverzeichnis und führen Sie aus:
# Install Python dependencies
uv sync
# Download Chromium browser for Playwright (required for Israel weather)
uv run playwright install chromiumDauert der erste Start etwas? Ja—Playwright lädt Chromium herunter (~300 MB). Dies ist ein einmaliger Vorgang.
2. OpenAI API-Schlüssel konfigurieren
Erstellen Sie eine .env-Datei im Projektstamm:
# Option A: Copy the template
copy .env.example .env
# Option B: Create manually
echo OPENAI_API_KEY=your_openai_api_key > .envErsetzen Sie your_openai_api_key durch Ihren tatsächlichen OpenAI API-Schlüssel von platform.openai.com/account/api-keys.
⚠️ Wichtig: Committen Sie niemals .env oder teilen Sie Ihren API-Schlüssel. Die .gitignore schließt ihn bereits aus.
3. Installation überprüfen
Testen Sie die Einrichtung:
uv run .\host.pySie sollten sehen:
Connected to server with tools: [...]
MCP Client Started!
Type your queries or 'quit' to exit.
Query:Drücken Sie Ctrl+C oder geben Sie quit ein, um zu beenden.
▶️ Agent ausführen
Starten Sie den interaktiven Chat:
uv run .\host.pyDer Agent startet im interaktiven Modus. Geben Sie eine Wetterfrage ein und drücken Sie Enter. Der Agent wird:
✅ Ihre Frage analysieren
🔍 An das passende Wetter-Tool weiterleiten (Israel oder USA)
🌐 Echtzeitdaten abrufen (kann ein Browserfenster für Israel öffnen)
📊 Die Antwort verarbeiten und formatieren
💬 Die Antwort zurückgeben
Geben Sie quit ein, um zu beenden.
Für Israel-Abfragen: Ein Chromium-Browserfenster öffnet sich automatisch. Das ist normal—der Agent verwendet Playwright, um mit Weather2day zu interagieren. Das Fenster schließt sich, wenn die Abfrage abgeschlossen ist.
🏗️ Architektur
┌─────────────────────────────────────────────────────────────────┐
│ OpenAI (GPT-4o-mini) │
│ (Tool Selection & Reasoning) │
└────────────────────────────┬────────────────────────────────────┘
│
┌────────────┴────────────┐
│ │
┌───────▼────────┐ ┌───────▼────────┐
│ MCP Host │ │ MCP Host │
│ (client.py) │ │ (client.py) │
└───────┬────────┘ └───────┬────────┘
│ │
┌───────▼──────────────┐ ┌──────▼─────────────┐
│ weather_Israel.py │ │ weather_USA.py │
│ (Playwright + Web) │ │ (NWS API) │
└───────┬──────────────┘ └──────┬─────────────┘
│ │
┌───────▼──────────────┐ ┌──────▼─────────────┐
│ Weather2day.co.il │ │ api.weather.gov│
│ (Live Web Browser) │ │ (REST API) │
└──────────────────────┘ └───────────────────┘Datenfluss:
Sie stellen eine Frage im Chat
Der Host sendet sie an OpenAI mit verfügbaren Tools
OpenAI wählt basierend auf der Frage aus, welches Tool aufgerufen werden soll
Der entsprechende MCP-Server führt aus und gibt Daten zurück
Der Host formatiert und gibt die Antwort zurück
💬 Beispielabfragen
🇮🇱 Israel-Wetter
What is the weather forecast for Jerusalem?
Give me the forecast for Tel Aviv.
What is the weather like in Haifa today?
Show me the forecast for Eilat.
What is the weather in Beer Sheva?
Is it going to rain in Kfar Saba tomorrow?
Tell me about the weather in Nazareth.
What's the forecast for Tiberias?Unterstützte Städte: Jerusalem, Tel Aviv, Haifa, Eilat, Beerscheba, Kfar Saba, Tiberias, Nazareth und die meisten größeren israelischen Städte, die von Weather2day erkannt werden.
🇺🇸 USA-Wetter
What is the weather forecast for New York City? (40.7128, -74.0060)
What's the weather at latitude 34.0522, longitude -118.2437? (Los Angeles)
Are there any active weather alerts in California?
What weather alerts are in effect in New York?
Give me the forecast for Chicago coordinates. (41.8781, -87.6298)
Are there any severe weather warnings in Texas?Format:
Vorhersagen: Verwenden Sie Breiten-/Längengrad-Koordinaten
Warnungen: Verwenden Sie zweistellige Bundesstaatscodes (
CA,NY,TXusw.)
🎯 Intelligentes Agentenverhalten
Der Agent versteht Kontext:
✅ "Wie ist das Wetter in Jerusalem?" → Verwendet Israel-Tool
✅ "Ich besuche New York, wie ist die Vorhersage?" → Verwendet USA-Tool (40.71, -74.01)
✅ "Erzähl mir vom Wetter in Tel Aviv und Los Angeles" → Ruft beide Tools auf
✅ "Regnet es gerade in Chicago?" → Leitet an USA-Tool weiter
📂 Projektstruktur
MCP Weather/
├── host.py # Main chat host (OpenAI + tool orchestration)
├── client.py # MCP client connector
├── weather_Israel.py # Israel MCP server (Playwright + Web)
├── weather_USA.py # USA MCP server (NWS API)
├── pyproject.toml # Dependencies and project metadata
├── uv.lock # Locked dependency versions
├── .env.example # Template for environment variables
├── .env # Your API keys (git-ignored)
├── README.md # This file
└── .gitignore # Git exclusion rules🐛 Fehlerbehebung
❌ OPENAI_API_KEY fehlt
Problem: Der Agent kann Ihren API-Schlüssel nicht finden.
Lösung:
Überprüfen Sie, ob
.envim Projektstamm existiert (gleicher Ordner wiehost.py)Prüfen Sie, ob die Datei enthält:
OPENAI_API_KEY=sk-ihr-tatsaechlicher-schluesselStarten Sie den Host neu:
uv run .\host.py
❌ Chromium-Browser startet nicht
Problem: Fehler "Failed to launch browser".
Lösung:
uv run playwright install chromiumWenn es weiterhin fehlschlägt, prüfen Sie den Speicherplatz (Chromium benötigt ~500 MB).
❌ Verbindungszeitüberschreitung oder API-Fehler
Problem: "net::ERR_CONNECTION_RESET" oder Anforderungsfehler.
Lösung:
Überprüfen Sie Ihre Internetverbindung
Verifizieren Sie, dass Ihr OpenAI API-Schlüssel gültig ist unter platform.openai.com
Prüfen Sie API-Nutzung und Abrechnung
❌ Israel-Browser öffnet, aber Suche schlägt fehl
Problem: Die Stadtsuche liefert keine Vorschläge.
Erklärung: Die Weather2day-Autovervollständigung funktioniert nur auf der /forecast-Seite. Das Tool kehrt vor jeder Suche automatisch zu dieser Seite zurück, aber wenn es fehlschlägt:
Lösung:
Stellen Sie sicher, dass Chromium aktuell ist:
uv run playwright install --with-deps chromiumStellen Sie sicher, dass Sie Internetzugang zu
weather2day.co.ilhabenPrüfen Sie auf Browser-Pop-ups oder Cookie-Banner (der Agent behandelt diese, aber sehr langsame Verbindungen können zu Zeitüberschreitungen führen)
❌ "Ein Server kann keine Verbindung herstellen"
Problem: MCP-Server-Verbindungsfehler.
Lösung:
# Run from the project directory
Set-Location .\project-template
uv run .\host.py❓ FAQ
F: Wie entscheidet der Agent, welches Tool verwendet wird?
A: OpenAIs GPT-4o-mini liest Ihre Frage und wählt das beste Tool basierend auf dem Kontext (erwähntes Land/Region, bereitgestellte Koordinaten usw.). Es ist nicht regelbasiert—es verwendet Sprachverständnis.
F: Kann ich in einer Abfrage nach mehreren Regionen fragen?
A: Ja! Der Agent kann mehrere Tools aufrufen. Versuchen Sie: "Wie ist das Wetter in Tel Aviv und New York?"
F: Warum öffnet sich der Israel-Browser sichtbar?
A: Playwright automatisiert Weather2day, indem es in die Live-Suche tippt, Ergebnisse auswählt und die Seite liest. Der Headless-Modus (unsichtbar) funktioniert mit der JavaScript-lastigen Autovervollständigung dieser Website nicht, daher läuft der Browser sichtbar.
F: Wie viel kostet das?
A: Sie werden von OpenAI basierend auf API-Aufrufen abgerechnet. Jede Abfrage kostet ~$0.001–$0.01 je nach Antwortlänge. Israel-Web-Scraping ist kostenlos; USA-API ist kostenlos (NWS ist öffentlich).
F: Was, wenn ich weitere Regionen hinzufügen möchte?
A: Erstellen Sie einen neuen MCP-Server (z. B. weather_Europe.py) und registrieren Sie ihn in host.py unter self.mcp_clients. Siehe die vorhandenen Server für das Muster.
F: Ist mein API-Schlüssel sicher?
A: Ja. Ihre .env ist von Git ausgeschlossen und wird nur lokal gespeichert. Geben Sie Ihren Schlüssel niemals in Code oder öffentlichen Chats preis.
F: Kann ich das in der Produktion verwenden?
A: Dies ist ein Demo-Projekt. Für die Produktion:
Verwenden Sie asynchrone Anforderungswarteschlangen
Fügen Sie Protokollierung und Fehlerwiederherstellung hinzu
Zwischenspeichern Sie Vorhersagen (Wetter ändert sich stündlich, nicht pro Abfrage)
Richten Sie Überwachung und Warnungen ein
Verwenden Sie umgebungsbasierte Konfiguration
🔒 Sicherheit
⚠️ Tun | ❌ Nicht tun |
✅ | ❌ |
✅ API-Schlüssel regelmäßig rotieren | ❌ Ihren API-Schlüssel teilen |
✅ Umgebungsvariablen in der Produktion verwenden | ❌ Geheimnisse im Code hartkodieren |
✅ API-Abrechnungslimits prüfen | ❌ Kontingent unbegrenzt lassen |
✅ HTTPS für API-Aufrufe verwenden | ❌ HTTP in der Produktion verwenden |
Wenn Ihr API-Schlüssel offengelegt wurde:
Widerrufen Sie ihn sofort im OpenAI-Dashboard
Generieren Sie einen neuen Schlüssel
Aktualisieren Sie
.env
📝 Lizenz
Dieses Projekt wird für Bildungszwecke ohne Gewähr bereitgestellt. Details finden Sie in der LICENSE.
🤝 Mitwirken
Haben Sie Ideen zur Verbesserung des Agents? Beiträge sind willkommen!
Forken Sie das Repository
Erstellen Sie einen Feature-Branch (
git checkout -b feature/amazing-feature)Committen Sie Ihre Änderungen (
git commit -m 'Add amazing feature')Pushen Sie den Branch (
git push origin feature/amazing-feature)Öffnen Sie einen Pull Request
📞 Support
🐛 Melden Sie Fehler über GitHub Issues
💡 Schlagen Sie Funktionen in Discussions vor
📧 Für Sicherheitsprobleme senden Sie eine private E-Mail (eröffnen Sie kein öffentliches Issue)
Viel Spaß beim Vorhersagen! 🌦️☀️
Available Tools
4 toolsenter_weather_forecast_city_israelC
מקבל שם עיר ומזין אותו בשדה החיפוש באתר מזג האוויר.
:param city_name: שם העיר בחיפוש (למשל: 'ירושלים', 'תל אביב')
| Name | Required | Description | Default |
|---|---|---|---|
| city_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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. It discloses only that it fills a search field; it does not say whether the search is submitted, what happens if the city is not found, whether the site must already be open, or what state the page is left in for the next sibling call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short lines with the action stated first and the parameter documented in a conventional :param block; nothing is padded. It is efficient, though the colon-prefixed param line is stylistically thin for a tool with a workflow dependency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need no explanation. However, for a browser-automation step in a four-tool sequence, the description omits the ordering/state assumptions an agent needs, leaving a real gap despite the adequate parameter hint.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for the single parameter, so the description must compensate, and it partially does by explaining that city_name is the search string and giving examples ('ירושלים', 'תל אביב'). It adds useful illustrative meaning but no format or validation constraints beyond that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a concrete verb and resource: it receives a city name and types it into the search field of the weather site. That is specific enough to distinguish a UI-entry step, though it never names or contrasts the sibling tools (select_weather_forecast_city_israel, open_weather_forecast_israel), so an agent must infer the ordering itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit when-to-use guidance, no prerequisites, and no mention of the sibling steps that presumably follow. The workflow role (enter search text, then select the city, then read content) is only implied by the tool names, not stated in the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_forecast_content_israelA
מחלקץ את הטקסט הגלוי מתוך דף התחזית הנוכחי שנפתח בדפדפן, מנקה רווחים מיותרים ומחזיר את התוכן ל-LLM לצורך מתן תשובה.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the extraction behavior and whitespace cleanup, which is useful, but omits what happens when no page is open, failure/error behavior, and any permission considerations. Adequate but incomplete for a zero-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence stating the action, with no wasted preamble. The trailing clause about returning content 'to the LLM to provide an answer' is mildly redundant but not harmful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be described, and there are no parameters to document. The main remaining gap is state-dependence (what it requires the browser to already be showing), which is only implied rather than guaranteed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero parameters, so the baseline of 4 applies. Nothing about parameters needs explaining and the description does not confuse the matter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb and resource: it extracts the visible text from the current forecast page open in the browser and returns a cleaned version. That is clear and distinguishable from open/enter/select siblings in the pipeline, though it never explicitly names how it differs from them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied rather than stated: it works on the page 'opened in the browser', so the agent can infer it must run after opening and selecting a city. There is no explicit statement of prerequisites, exclusions, or what to do instead if the page is not ready.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
open_weather_forecast_israelA
פותח את דפדפן האינטרנט ומנווט לדף הבית של אתר מזג האוויר הישראלי.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses the side effect that a browser window is opened, which is meaningful for an agent. It does not mention state (new tab vs. existing), auth, or what the user sees next, leaving gaps for a no-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler; the action and destination are stated immediately. Nothing needs trimming.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (0 params, output schema present so return values need no explanation) and the description adequately covers the action. The remaining gap is routing context against the sibling tools, which is a moderate omission rather than a fatal one.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, and the schema coverage is 100%, so there is nothing for the description to disambiguate. Baseline 4 applies since no parameter semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a concrete verb+resource in Hebrew: it opens the browser and navigates to the Israeli weather site's homepage. An agent can tell what the tool does. However, it never distinguishes itself from the three sibling tools (enter/select/get_weather_forecast_*), so it stops at 4.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The mention of navigating to the 'homepage' implies this is the entry/starting step before the city-selection and content siblings, but there is no explicit statement of when to use it versus enter_weather_forecast_city_israel or get_weather_forecast_content_israel. Usage is only inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
select_weather_forecast_city_israelA
בוחר את התוצאה הראשונה ברשימת הערים שנפתחה בעקבות החיפוש.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full disclosure burden. It usefully reveals that selection is deterministic ('first result'), meaning the agent cannot choose a specific city, but it omits what happens if the list is empty, whether it navigates to a new page, and any auth or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, tight sentence with no padding, and the key constraint ('first result') is placed prominently. Appropriate length for a zero-argument UI step.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists so return values need not be explained, and there are no parameters. What remains unstated is the workflow contract (what must precede this call, what state follows it), which matters for a sequential UI-automation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is nothing for the description to disambiguate. The baseline for a parameterless tool is 4, and the description adds no misleading parameter claims.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb (selects) and a specific target (the first result in the city list opened by the search), which distinguishes it from siblings like enter_weather_forecast_city_israel or open_weather_forecast_israel. It stops short of explicitly naming the alternative tools, but the action and target are clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'the city list that was opened following the search' implies this step follows a search and a list-opening step, giving sequencing context. However, it never states when to use this versus entering a city directly, nor any preconditions or failure conditions.
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.
4 tool updates
v0.1.0- First observed
enter_weather_forecast_city_israel - First observed
get_weather_forecast_content_israel - First observed
open_weather_forecast_israel - First observed
select_weather_forecast_city_israel
TDQS
Scored across 4 tools
Each tool maps to a distinct browser-automation step (open site, enter city, select result, extract content), so they are separable in principle. However, they form a rigid ordered pipeline with no way to tell from the names alone that they must be chained, and enter vs. select could be momentarily confused by a hurried agent.
All names are snake_case and share a predictable verb_..._israel suffix (open/enter/select/get_weather_forecast_..._israel). Minor structural deviation: open_weather_forecast_israel omits the 'city' segment the other three include, but the pattern is still readable and consistent.
Four tools is a reasonable count for this narrow scraping workflow. It is slightly granular, since a single 'fetch forecast for city' operation is split into four browser steps, but each step is individually actionable and the set stays well-scoped.
The surface covers the happy path open -> search -> select -> extract, but has no tool to close/reset the browser session, no error or no-result handling, and despite the generic server name 'MCP Weather Agent' it only supports Israeli locations. Agents can complete the core task but will hit dead ends on edge cases.
Maintenance
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