Llamaindex MCP Openweather Agent

This project demonstrates how to combine LlamaIndex with MCP tool integration to build an intelligent agent capable of real-time weather information query through the OpenWeather API.

Artificial intelligence chatbots Research and data #Weather query #MCP integration #Real-time data #Intelligent agent .Python

rating : 2 points

downloads : 6.5K

update time : 2025-12-12

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What is the MCP Weather Assistant?

This is an intelligent weather query system built based on the Model Context Protocol (MCP) and the LlamaIndex framework. It allows AI assistants (such as ChatGPT) to query real-time weather information for any location globally through simple conversations. The system connects to the OpenWeather API via the MCP server, encapsulating complex API calls into simple tool functions, making it easy for non-technical users to use.

How to use the Weather Assistant?

You only need to run the MCP server and then connect to this server in an AI assistant that supports MCP (such as Claude Desktop, Cursor, etc.). After that, you can ask weather questions just like having a conversation with an ordinary assistant, for example, 'What's the weather like in New York now?' or 'Will it rain in Tokyo tomorrow?' The system will automatically handle location recognition, API calls, and weather information formatting.

Applicable Scenarios

Suitable for various scenarios where quick access to weather information is needed: travel planning, outdoor activity arrangements, agricultural decision-making, logistics transportation, event planning, etc. Whether it's an individual user querying local weather or a business needing to integrate weather data into its workflow, this system can provide a convenient solution.

Main Features

Real-time Weather Query

Obtain real-time weather data for any location globally through the OpenWeather API, including detailed information such as temperature, humidity, wind speed, and weather conditions.

Intelligent Location Recognition

The system can understand location names in natural language and automatically recognize location information such as cities and countries without the user providing precise coordinates or codes.

MCP Protocol Integration

Use the Model Context Protocol standard to ensure compatibility with various AI assistants and development tools, providing a unified tool call interface.

LlamaIndex Intelligent Agent

An intelligent agent built based on the LlamaIndex framework, capable of understanding complex queries, handling multi-round conversations, and providing context-related weather information.

Easy to Expand

The modular design allows for easy addition of new weather data sources or expansion of query functions, such as air quality, UV index, weather forecasts, etc.

Advantages

No technical background required: Users can obtain weather information through natural language conversations without learning API calls or programming.

Real-time data: Connect directly to the OpenWeather API to ensure the latest weather information is obtained.

Global coverage: Supports querying the weather of almost all cities and regions globally.

Flexible integration: Can be integrated with various AI assistants and applications to provide a unified weather query interface.

Cost-effective: Built based on open-source technologies, reducing development and maintenance costs.

Limitations

Dependent on external API: Requires a stable OpenWeather API service, and network issues may affect availability.

API call limitations: The free version of the OpenWeather API has a daily call limit.

Location recognition accuracy: For cities with the same name or vague locations, users may need to further clarify.

Requires an API key: Users need to apply for and configure an OpenWeather API key themselves.

Limited historical data: Mainly provides current and short-term forecasts, with limited historical weather data functions.

How to Use

Environment Preparation

Ensure that your computer has Python 3.8 or a higher version installed and prepare an OpenWeather API key. If you don't have an API key, you need to register and obtain one on the OpenWeather official website first.

Download and Installation

Clone the project code repository and install the necessary dependency packages. It is recommended to use a virtual environment to avoid dependency conflicts.

Configure Environment Variables

Copy the environment variable template file and fill in your OpenWeather API key and other necessary configurations.

Start the MCP Server

Run the MCP server, which will serve as the backend service for the weather query tool. After the server starts, it will wait for connections from AI assistants.

Connect to the AI Assistant

Configure the MCP server address in an AI assistant that supports MCP (such as Claude Desktop), and then you can start querying the weather.