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VenkatAdena

Global Weather MCP Server

by VenkatAdena
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Global Weather MCP Server

A Model Context Protocol (MCP) server that gives AI assistants access to weather data without API keys.

Requirements

Requirement

Version

Python

3.14+

uv

Latest

Claude Desktop or another MCP client

Latest

Check your local versions:

python --version
uv --version

Related MCP server: weather

Installation

git clone https://github.com/YOUR-USERNAME/global-weather-mcp-server.git
cd global-weather-mcp-server
uv sync

No API keys are required.

Connect to Claude Desktop on Windows

For the Microsoft Store/package install of Claude Desktop, open this config file:

%LOCALAPPDATA%\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.json

Add only this server entry inside the mcpServers object:

{
  "mcpServers": {
    "global-weather": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\path\\to\\weather-mcp",
        "run",
        "global_weather_mcp_server.py"
      ]
    }
  }
}

If the config file already has other MCP servers, keep them and add only the global-weather block. Restart Claude Desktop after saving the file. The server should expose these tools:

  • get_alerts

  • get_forecast

  • get_forecast_global

Tools

get_alerts

Get active weather alerts for a US state.

Example:

What are the active weather alerts in California?

get_forecast

Get a detailed National Weather Service forecast for a US location by latitude and longitude.

Example:

What's the weather forecast for latitude 40.71, longitude -74.01?

get_forecast_global

Get current conditions and a 5-day forecast for any location worldwide by latitude and longitude.

Examples:

What's the weather forecast for latitude 19.0760, longitude 72.8777?
Get the weather for latitude 28.6139, longitude 77.2090.

Useful Indian city coordinates:

City

Latitude

Longitude

New Delhi

28.6139

77.2090

Mumbai

19.0760

72.8777

Bangalore

12.9716

77.5946

Chennai

13.0827

80.2707

Kolkata

22.5726

88.3639

Hyderabad

17.3850

78.4867

Pune

18.5204

73.8567

Test Locally

Run the MCP server directly:

uv run global_weather_mcp_server.py

If it starts and waits silently, the server is ready for an MCP client.

Project Structure

global-weather-mcp-server/
|-- global_weather_mcp_server.py
|-- pyproject.toml
|-- uv.lock
|-- README.md
`-- .gitignore

Available Tools

3 tools
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.

get_forecastC

Get weather forecast for a location.

Args: latitude: Latitude of the location longitude: Longitude of the location

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description must carry behavioral context. It only says 'get' a forecast and gives no indication of units, time range, coordinate format, or whether this is a read-only operation. Nothing contradicts annotations, but little is disclosed beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and the core purpose is front-loaded. The Args block is somewhat redundant with the schema but does not add significant bloat, keeping the overall entry compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides the essential call requirements (latitude and longitude) and the presence of an output schema reduces the need to document return values. However, it omits practical context like expected coordinate units, available forecast periods, and why an agent would choose this over get_alerts.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The Args section repeats the parameter names with minimal glosses ('Latitude of the location'), adding almost no meaning beyond the schema titles. Since schema description coverage is 0%, the description should compensate with coordinate format or range details, but it does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb and resource: 'Get weather forecast for a location'. It does not explicitly mention the sibling get_alerts, but the forecast-vs-alerts distinction is clear enough from the domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool instead of get_alerts, nor are any exclusions or alternative conditions provided. The intended usage is only implied by the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_forecast_globalA

Get weather forecast for any location worldwide including India.

Uses the Open-Meteo API (free, no API key required).

Args: latitude: Latitude of the location (e.g. 28.6139 for New Delhi) longitude: Longitude of the location (e.g. 77.2090 for New Delhi)

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses that the tool uses a free API requiring no authentication, which is positive. However, it does not mention rate limits, error handling for invalid coordinates, or response structure. Still, it provides useful behavioral context.

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: two short paragraphs with no unnecessary words. It front-loads the purpose and includes essential usage notes. Every sentence is earned.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (2 params, no nested objects) and the presence of an output schema, the description is fairly complete. It explains the API source and coordinate examples. Minor gaps like timezone or units are acceptable for a simple forecast tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so the description's 'Args' section adds value by providing example values (e.g., 28.6139 for New Delhi). This compensates for the lack of schema descriptions and clarifies the coordinate format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get weather forecast for any location worldwide including India', which specifies the verb and resource. The name 'get_forecast_global' and the sibling 'get_forecast' hint at a regional distinction, but the description does not explicitly differentiate usage, so it's not a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions it uses the free Open-Meteo API with no API key, but does not provide explicit when-to-use or when-not-to-use guidance relative to siblings. This is minimal but adequate.

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. 3 tool updatesv0.1.0
    • First observedget_alerts
    • First observedget_forecast
    • First observedget_forecast_global
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

B3.2/5.0

Scored across 3 tools

Disambiguation3/5

get_alerts is distinct for US alerts, but get_forecast and get_forecast_global both provide forecasts; their regional scope is unclear from descriptions, causing potential confusion.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (get_alerts, get_forecast, get_forecast_global).

Tool Count3/5

With only 3 tools, the server is minimal but covers basic weather needs; it's borderline but not extreme.

Completeness3/5

Covers alerts and forecasts, but lacks current conditions, historical data, and other common weather features, leaving notable gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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