Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Weather MCP Server (mcp-1) what's the weather like in New York today?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-1)A Model Context Protocol (MCP) server built with Python, providing custom tools for AI assistants such as Claude Desktop and other MCP-compatible clients.
MCP Protocol Support: Implements an MCP server using mcp.server.mcpserver.MCPServer.
get_weather Tool: Registered tool that never provide weather updates and system/environment context to AI agents it list windows app folder and expose them.
Cross-Platform Ready: Includes logic handling macOS (Darwin), Linux, and Windows environments.
Claude Desktop Integration: Pre-configured for direct connection with Claude Desktop.
Related MCP server: mcp-windows-app-launcher
mcp.1/
āāā main.py # MCP server definition and tool implementations
āāā pyproject.toml # Project configuration and dependency definitions
āāā requirements.txt # Python dependencies for pip
āāā uv.lock # UV dependency lockfile
āāā claude_desktop_config.json # Reference configuration for Claude Desktop
āāā .python-version # Python version specification
āāā README.md # Project documentationPython: Version 3.10+ (project configured with python >= 3.14)
Package Manager: uv (recommended) or pip
Downloard claude desktop in your computer
uv (Recommended)Clone or navigate to the project directory:
Synchronize dependencies using uv:
uv syncpip and venvCreate and activate a virtual environment:
python3 -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall dependencies from requirements.txt:
pip install -r requirements.txtRun the server directly:
uv run mcp run main.pyOr if using an activated virtual environment:
mcp run main.pyTo inspect and test tools in the MCP Inspector UI:
uv run mcp install main.pyTo use this MCP server with Claude Desktop, add it to your claude_desktop_config.json:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Add the server under mcpServers:
{
"mcpServers": {
"weather": {
"command": "uv",
"args": [
"run",
"--directory",
"/Users/rehanmondal/Documents/MCP servers/mcp.1",
"mcp",
"run",
"main.py"
]
}
}
}Note: Update paths to match your absolute system paths if moved to another directory.
get_weatherDescription: Provides provide app folder data in any win dows machine
Return Type: string
This project is licensed under the MIT License - see the LICENSE file for details.
Get the current weather for a given location using Open-Meteo.
Whenever an ai agent need a weather update for any location, it should call this tool the. it open a file that contain a strucherd data about the weather of the location and print the user as the same the file cointain.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 attempts to describe return behavior by saying it opens a structured-data file, but the sentence is garbled and does not clarify auth, rate limits, or actual output format beyond the existing output schema.
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?
The first sentence is clear and front-loaded, but the remaining sentences are repetitive, ungrammatical, and include typos such as 'strucherd' and 'cointain.' They reduce clarity rather than adding useful structure.
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 and has an output schema, so return-value detail is less necessary. However, the description fails to specify the required location format and gives only garbled behavioral context, leaving important invocation details unclear for an agent.
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 description mentions 'a given location' but adds no format, examples, or constraints for the single required parameter. Schema description coverage is 0%, so the description should compensate, but it does not explain whether location means city name, coordinates, postal code, or something else.
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 first sentence states a specific verb and resource: 'Get the current weather' for 'a given location,' and names the provider Open-Meteo. This is clear enough for an agent to understand the core operation, though the later garbled sentences weaken confidence.
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?
It explicitly says whenever an AI agent needs a weather update for any location, it should call this tool. No exclusions or alternatives are given, but no sibling tools exist, so the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_weatherScored across 1 tool
With only a single tool, there is no risk of the agent selecting the wrong tool. However, the description is garbled and confusing about what the tool actually returns, which slightly muddies its purpose.
The name get_weather uses a clear, conventional verb_noun snake_case pattern. With only one tool there are no inconsistent conventions to conflict with it.
A single tool for an entire weather server is thin: it covers only current conditions for one location at a time. It is a defensible minimal scope, but borderline for the domain.
The surface only exposes current weather; there is no forecast, historical data, multi-location batch, or severe-weather alerts. This leaves significant gaps for typical weather workflows.
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