MCP US Weather Client/Server

This was my first encounter with hands on implementation of MCP, i really liked it & i firmly believe MCP is here to stay, i have to implement some more projects to get more more better understanding about the overall ecosystem,About the repository, This repository contains a simple MCP (Model Context Protocol) server that provides weather alert information for US states, leveraging the National Weather Service (NWS) API. It also includes an example client application that demonstrates how to interact with the MCP server using the mcp_use library and Langchain.

Features

Weather Alerts: Fetches and formats active weather alerts for a specified US state.
MCP Integration: Demonstrates how to build an MCP server using the fastmcp library.
Client Example: Provides a client application that uses the server and integrates conversation memory.
Langchain Integration: Example showcases LLM integration.

Architecture

graph LR
%% MCP System

    subgraph MCP_System

        A[Client: client.py]

        B[MCP Agent: mcp_use.MCPAgent]

        C[MCP Client: mcp_use.MCPClient]

        D[FastMCP Server: weather.py]

        E[NWS API: api.weather.gov]
A --> B

        B --> C

        C --> D

        D --> E

    end
%% Data Flow

    subgraph Data_Flow

        F[User Input]

        F --> A

        E -->|Geo+JSON| D

        D -->|Weather Data| B

        B -->|LLM Interaction| F

    end
%% Styling with visible text

    classDef client fill:#f9f,stroke:#333,color:#000,font-weight:bold;

    classDef agent fill:#ccf,stroke:#333,color:#000,font-weight:bold;

    classDef server fill:#fcc,stroke:#333,color:#000,font-weight:bold;

    classDef api fill:#ffc,stroke:#333,color:#000,font-weight:bold;

    classDef user fill:#cfc,stroke:#333,color:#000,font-weight:bold;
class A client;

    class B,C agent;

    class D server;

    class E api;

    class F user;
%% Link styling

    linkStyle default stroke:#000,stroke-width:2px;

Prerequisites

Before running the server and client, ensure you have the following installed:

Python 3.7+
uv (recommended for dependency management - https://github.com/astral-sh/uv)
Groq API key. This needs to be added to .env file.
mcp_use Library: Make sure you've installed this according to its documentation.

Installation

1. Clone the repository:

    git clone <your_repository_url>
    cd <repository_directory>
    

2. Install dependencies (using uv):

    uv add -r requirements.txt
    

This command uses uv pip install . to install the project and its dependencies based on the pyproject.toml file in the current directory.

3. Create .env file:
Create a .env file in the root directory with the following content, replacing <YOUR_GROQ_API_KEY> with your actual Groq API key:

    GROQ_API_KEY=<YOUR_GROQ_API_KEY>
    

Usage

Running the MCP Weather Server & Client

1. Start the server:

    uv run mcp dev server/weather.py
    

1. Start the Client:

    uv run server/client.py

    

Interacting with the Client

Type your queries at the prompt. For example: "Get weather alerts for CA, NY or NJ etc".
Type exit or quit to end the conversation.
Type clear to clear the conversation history.

Console Output: This screenshot shows the typical output of the client application during a conversation, demonstrating how the agent interacts with the MCP server and utilizes the get_alerts tool.

• MCP Inspector: This screenshot shows the typical weather.json used with the MCP Inspector

Contact

For any inquiries or support, please reach out:

- Email: business@aiankit.com
- Personal Website: aiankit.com