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 What's the weather forecast for Denver?"
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.
A Model Context Protocol (MCP) server built with FastMCP that provides US weather forecasts and active alerts using the National Weather Service (NWS) API.
When connected to an AI assistant, it provides the following tools:
get_alerts(state: str): Fetches active weather alerts for a US state (e.g. "CA", "NY").
get_forecast(latitude: float, longitude: float): Fetches detailed weather forecasts for a specific geographic coordinate.
Related MCP server: MCP Weather Server
weather.py: The main FastMCP server containing the API calls and tool definitions.
.venv/: The Python virtual environment for isolated dependencies.
pyproject.toml / main.py: Other configuration and entry point scripts.
Make sure you are using the virtual environment:
source .venv/bin/activateRequired packages are likely already installed, but if not:
pip install "mcp[cli]" httpxYou can test the MCP tools locally through a web interface using the MCP Inspector:
npx @modelcontextprotocol/inspector "/Users/name/mcp servers/.venv/bin/python" "/Users/name/mcp servers/weather.py"To use this server with Claude Desktop, add the following to your claude_desktop_config.json file:
{
"mcpServers": {
"weather": {
"command": "/Users/name/mcp servers/.venv/bin/python",
"args": [
"/Users/name/mcp servers/weather.py"
]
}
}
}Restart Claude and it will now have access to live formatting and tracking for US weather!
Get weather alerts for a US state.
Args: state: Two-letter US state code (e.g. CA, NY)
| Name | Required | Description | Default |
|---|---|---|---|
| state | 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. '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.
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.
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.
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.
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.
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 weather forecast for a location.
Args: latitude: Latitude of the location longitude: Longitude of the location
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_alertsget_forecastScored across 2 tools
The two tools serve entirely different purposes: one for alerts by state, another for forecast by coordinates. No overlap or confusion possible.
Both tools follow a consistent verb_noun pattern (get_alerts, get_forecast), making it easy to predict naming.
With only 2 tools, the server feels thin compared to typical weather API capabilities. It's borderline but not excessive given its narrow scope.
The server lacks common weather operations like current conditions, hourly forecast, or historical data. Significant gaps exist for a comprehensive weather service.
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.