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weather-mcp-a2a

Verified Safe

by manuelalba1021

Overview

An agentic AI system that fetches and reasons over real-time global weather data using the Model Context Protocol (MCP) and large language models.

Installation

Run Command
streamlit run Weather_streamlit_app.py

Environment Variables

  • GROQ_API_KEY

Security Notes

The project adheres to good security practices by loading API keys from environment variables (`.env`). External API calls are made using `httpx` and `requests` to well-known weather APIs (`api.weather.gov`, `open-meteo.com`), which mitigates direct network risks. Input sanitization for city names used in API requests relies on URL parameter encoding by the `requests` library, which is generally robust against injection. No use of `eval` or other dangerous code execution patterns was identified. The most significant concern, noted in the project's internal documentation, is the unreliability of certain LLMs (Groq models) in correctly formatting tool calls, which led to a direct API implementation workaround in the main Streamlit app. This workaround itself is implemented securely, but deviates from the intended agentic workflow.

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Stats

Interest Score0
Security Score8
Cost ClassMedium
Avg Tokens200
Stars0
Forks0
Last Update2026-01-19

Tags

Agentic AIWeather DataReal-TimeLLMMCPStreamlit