Built using Node.js to implement MCP tools for weather data retrieval from the US National Weather Service API.
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., "@MCP Weather Server get weather alerts for California"
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.
This project is a demo implementation of a Model Context Protocol (MCP) server that provides weather-related tools. The server exposes two tools:
get-alerts: Fetches active weather alerts for a given US state.
get-forecast: Provides a weather forecast for a specific location based on latitude and longitude.
Built using Node.js.
Implements MCP tools for weather data retrieval.
Uses the US National Weather Service API for accurate and up-to-date weather information.
Related MCP server: Weather MCP Server
Node.js installed on your system.
Familiarity with MCP concepts and tools.
To install mcp-server-learn for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @adarshem/mcp-server-learn --client claudeClone the repository:
git clone <repository-url>
cd weatherInstall dependencies using pnpm (as configured in the project):
pnpm installBuild the project:
pnpm buildUpdate your settings.json file of VSCode to add this MCP server
{
"mcpServers": {
"weather": {
"command": "node",
"args": [
"/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js"
]
}
}
}Get weather alerts for a state
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code (e.g. CA, NY) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it behaves - no information about response format, error conditions, rate limits, authentication requirements, or whether it's a read-only operation.
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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a simple tool with one 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?
For a simple read operation with one well-documented parameter and no output schema, the description provides basic functionality but lacks important context about response format and behavioral characteristics that would help an agent use it effectively.
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 has 100% description coverage, with the parameter 'state' clearly documented as a two-letter code. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline for high schema coverage.
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 action ('Get') and resource ('weather alerts for a state'), making the purpose immediately understandable. It doesn't explicitly distinguish from the sibling tool 'get-forecast', but the focus on alerts vs. forecast provides implicit differentiation.
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 explicit guidance is provided about when to use this tool versus the sibling 'get-forecast' tool. The description implies it's for weather alerts specifically, but there's no mention of alternatives, prerequisites, or exclusions.
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
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude of the location | |
| longitude | Yes | Longitude of the location |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but fails to describe key behavioral traits such as whether it's a read-only operation, potential rate limits, authentication requirements, error handling, or the format of returned data. This leaves significant gaps for an agent to understand how to interact with it effectively.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for a simple tool, with every part of the sentence contributing essential information. No waste or redundancy is present.
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?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex weather data. It does not explain what the forecast includes (e.g., temperature, precipitation), timeframes, or data format. For a tool with no structured output documentation, the description should provide more context to help the agent understand the return values and usage implications.
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 input schema has 100% description coverage, with clear documentation for both parameters (latitude and longitude). The description does not add any additional meaning beyond what the schema provides, such as explaining coordinate systems or units. However, with high schema coverage, the baseline score of 3 is appropriate as the schema adequately handles parameter semantics.
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 tool's purpose with a specific verb ('Get') and resource ('weather forecast for a location'). It distinguishes from the sibling tool 'get-alerts' by focusing on forecasts rather than alerts, though the distinction could be more explicit. The description avoids tautology by not simply restating the name.
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 the sibling 'get-alerts' or any other alternatives. It lacks context about use cases, prerequisites, or exclusions. The agent must infer usage solely from the tool name and description without explicit direction.
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.
v1.0.0get-alertsget-forecastThe two tools have clearly distinct purposes: get-alerts focuses on weather alerts for a state, while get-forecast provides weather forecasts for a location. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the need for alerts versus forecasts.
Both tool names follow a consistent verb_noun pattern with hyphens (get-alerts and get-forecast). This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions.
With only two tools, the server feels thin for a weather domain, as it lacks essential operations like current conditions, historical data, or radar information. While the tools are well-defined, the count is too low to provide comprehensive coverage for typical weather-related tasks.
The tool set is severely incomplete for a weather server, missing core functionalities such as current weather conditions, historical data, radar maps, and air quality information. This limited surface will likely cause agent failures when users request common weather data beyond alerts and forecasts.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
NOAA and ECMWF weather forecast MCP for discovery, validation, and GribStream OAuth queries.
An MCP server for weather information by @kulybaba
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/adarshem/mcp-server-learn'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
