Click on "Install 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 bears full responsibility for behavioral disclosure. It fails to mention any side effects, authentication needs, rate limits, or data freshness. The description merely restates the function without 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, no unnecessary words. It is front-loaded with the purpose and directly addresses the parameter.
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?
While the tool has an output schema and only one parameter, the description does not elaborate on the nature of the alerts (e.g., severity, types) or any limitations. It is minimally adequate but could be more informative.
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?
With 0% schema description coverage, the description adds meaningful value by specifying the parameter format ('Two-letter US state code e.g., CA, NY'). This is informative and compensates for the sparse schema.
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 clearly states the verb 'Get', the resource 'weather alerts', and the scope 'for a US state'. This distinguishes it from the sibling tool 'get_forecast'.
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?
The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It only states the tool's basic function, leaving the agent to infer usage context.
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?
No annotations are provided, and the description does not disclose any behavioral traits such as read-only nature, rate limits, or data freshness. The description adds no behavioral context beyond the basic function.
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 two sentences, front-loaded with the main purpose. Every word is necessary and there is no redundancy.
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?
For a simple tool with two parameters and an output schema, the description is adequate but lacks details on forecast scope (e.g., time period, units). The presence of an output schema partially compensates, but the description could be more helpful.
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 schema description coverage is 0%, and the tool description merely repeats the parameter names and types ('latitude of the location', 'longitude of the location') without adding any additional meaning, format restrictions, or range constraints.
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 clearly states 'Get weather forecast for a location', using a specific verb and resource. It also distinguishes from the sibling tool 'get_alerts' which likely returns alerts, not forecasts.
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 on when to use this tool versus alternatives like get_alerts. There is no mention of prerequisites, limitations, or context for choosing between tools.
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. Dates show when Glama detected each change.
v0.1.0get_alertsget_forecastThe 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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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