Skip to main content
Glama
264Gaurav

Weather MCP Server

by 264Gaurav
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

MCP Server Creation and MCP client connection (Agent-Claude,cursor or, custom server calling and integration with local LLMs).

This project demonstrates how to create and connect an MCP (Model Context Protocol) weather server using FastMCP.

šŸ“– Want to learn more about MCP? Check out our Comprehensive MCP Guide for detailed explanations, concepts, and best practices.

Prerequisites

  • Python 3.13+

  • uv package manager installed

Related MCP server: Example MCP Server

Setup Instructions

1. Initialize the Project

Initialize a new project using uv:

uv init

2. Create Virtual Environment

Create a virtual environment for the project:

uv venv

3. Activate Virtual Environment

Activate the virtual environment:

Windows:

.venv\Scripts\activate

Linux/Mac:

source .venv/bin/activate

4. Install MCP CLI

Add the MCP CLI package to enable fast MCP CLI commands:

uv add "mcp[cli]"

Running the MCP Server

Development Mode

To run the MCP server in development mode:

uv run mcp dev server/weather.py

Expected Output:

Starting MCP inspector...

āš™ļø Proxy server listening on localhost:6277

Connecting to Claude Desktop

Install Server to Claude

Add the weather server to Claude Desktop:

uv run mcp install server/weather.py

Expected Output:

Added server 'weather' to Claude config 

Successfully installed weather in Claude app

Once installed, you can ask Claude questions like:

  • "What are the weather alerts in CA?"

  • The MCP server will be called automatically and display the results.

Manual Configuration for Cursor or Other Clients

For Cursor or other clients, you'll need to manually configure the server using the Claude Desktop config file format.

Get the server configuration from: claude_desktop_config file

Example Configuration:

"weather": {
  "command": "C:\\Users\\gaura\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\uv.EXE",
  "args": [
    "run",
    "--with",
    "mcp[cli]",
    "mcp",
    "run",
    "C:\\Users\\gaura\\OneDrive\\Desktop\\AI projects\\mcp_project\\server\\weather.py"
  ]
}

Note: Update the paths in the configuration to match your system paths.

Usage in Cursor:

  • Go to MCP ā†’ mcp config.json

  • Add the configuration above

Using MCP with Local LLMs (mcp-use)

mcp-use allows you to connect to MCP servers directly without an AI agent for programmatic tool access. This is useful for custom server calling and integration with local LLMs.

Installation

Install the mcp-use package:

uv add mcp-use

Configuration

Create a file named weather.json and add the following configuration to connect local LLMs to the MCP server:

{
  "mcpServers": {
    "weather": {
      "command": "C:\\Users\\gaura\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\uv.EXE",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "C:\\Users\\gaura\\OneDrive\\Desktop\\AI projects\\mcp_project\\server\\weather.py"
      ]
    }
  }
}

Note: Update the paths in the configuration to match your system paths.

References

Available Tools

1 tool
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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • First observedget_alerts
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

A3.6/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

Since there is only one tool, it inherently follows a consistent pattern with itself. The naming uses a verb_noun format (get_alerts), which is clear and standard, and there are no other tools to introduce inconsistency.

Tool Count2/5

A single tool is too few for a weather server, as it severely limits functionality. For a weather domain, agents would expect basic operations like getting current conditions, forecasts, or historical data, but this server only provides alerts for US states, making it incomplete and under-scoped.

Completeness1/5

The tool set is severely incomplete for a weather server. It only covers weather alerts for US states, missing core weather functionalities such as current weather, forecasts, or data for other regions. This creates significant gaps that will likely cause agent failures when broader weather queries are made.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

    suryanshp1
    F
    license
    B
    quality
    D
    maintenance
    A simple demonstration server for the MCP Python SDK that provides weather alerts for locations, allowing users to query weather information through Claude Desktop or Cursor.
    1
    -

  • MCP Quickstart Weather Server

    veysby
    F
    license
    B
    quality
    D
    maintenance
    A basic MCP server adapted from the official quickstart guide that provides weather data functionality and works with OpenAI chat completions API. Demonstrates MCP server setup with configuration examples for Claude Desktop and development tools.
    2
    -

  • mcp-weather

    minwoo129
    F
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server built with the mcp-framework to provide weather-related tools and data to AI clients. It enables integration of weather capabilities and custom tools into the MCP ecosystem for use with platforms like Claude Desktop.
    7 npm
    -