Weather MCP Server
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 Serverwhat's the weather in New York City right now?"
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.
Weather MCP Server
This project is a Model Context Protocol (MCP) server that provides weather information.
Features
Get current weather forecast for a specific latitude and longitude.
Get active weather alerts for a US state.
Related MCP server: Weather MCP Server
Setup
Install dependencies:
pnpm installBuild the server:
pnpm run build
Running the Server
This server is designed to be run by an MCP client, such as Claude for Desktop.
To configure Claude for Desktop (or a similar MCP client) to use this server, you'll need to point it to the built server. The typical configuration would involve specifying:
Command:
nodeArguments:
["/ABSOLUTE/PATH/TO/YOUR/PROJECT/mcptest/build/index.js"]
Replace /ABSOLUTE/PATH/TO/YOUR/PROJECT/ with the actual absolute path to the mcptest directory on your system.
For example, if your project is in /Users/bohe/Desktop/mcptest, the argument would be ["/Users/bohe/Desktop/mcptest/build/index.js"].
Refer to your MCP client's documentation for specific instructions on how to add and configure an MCP server.
Development
Source code is in the
srcdirectory.The main server logic is in
src/index.ts.Build output is in the
builddirectory.
MCP Configuration
The .vscode/mcp.json file is provided for VS Code to recognize and potentially debug this MCP server.
Available Tools
2 toolsget-alertsC
Get weather alerts for a state
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code (e.g. CA, NY) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.
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 alert data. It doesn't explain what the alerts include, how they're formatted, or any limitations, leaving the agent with insufficient context for effective use.
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 doesn't add any parameter-specific information beyond what's in the input schema, which has 100% coverage and fully documents the 'state' parameter. This meets the baseline score of 3, as the schema adequately handles parameter semantics without needing extra description.
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') and resource ('weather alerts for a state'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling tool 'get-forecast', which likely provides different weather data, so it doesn't reach the highest score.
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 its sibling 'get-forecast' or any alternatives. It lacks context about usage scenarios, exclusions, or prerequisites, offering only a basic statement of function.
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
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude of the location | |
| longitude | Yes | Longitude of the location |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe any behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or what the response format might be. This is inadequate for a tool with zero annotation coverage.
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, clear sentence that efficiently conveys the core purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 that there are no annotations and no output schema, the description is incomplete. It doesn't provide enough context about behavioral aspects, response format, or how this tool differs from its sibling. For a tool with this level of complexity and lack of structured data, the description should do more to compensate.
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 100%, with both parameters (latitude and longitude) well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema, so it meets the baseline score of 3.
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') and resource ('weather forecast for a location'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get-alerts', which likely provides different weather-related information, so it doesn't achieve the highest score.
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. It doesn't mention the sibling 'get-alerts' or explain the difference between getting a forecast versus alerts, leaving the agent without context for tool selection.
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.
2 tool updates
- First observed
get-alerts - First observed
get-forecast
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one retrieves weather alerts for a state, while the other provides 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 general weather predictions.
Both tools follow a consistent verb_noun naming pattern with hyphens (get-alerts and get-forecast). This uniformity makes the tool set predictable and easy to understand, adhering to a clear convention throughout.
With only 2 tools, the server feels thin for a weather domain, lacking essential operations like current conditions, historical data, or radar imagery. While the tools are well-defined, the count is too low to adequately cover typical weather-related use cases, suggesting an incomplete surface.
The tool set is severely incomplete for a weather server, missing core functionalities such as current weather conditions, historical data, or severe weather details. Agents will face significant gaps when trying to perform common weather-related tasks, leading to potential failures in broader workflows.
Maintenance
Related MCP Connectors
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
Related MCP Servers
- AlicenseBqualityDmaintenanceA Model Context Protocol server that provides tools to fetch weather alerts for US states and forecasts based on latitude/longitude coordinates using the US National Weather Service API.298 npm1MIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables AI models to fetch weather alerts and detailed forecasts for US locations using the National Weather Service API.98 npmGPL 3.0
- AlicenseBqualityCmaintenanceA Model Context Protocol server that provides weather information and forecasts based on user location or address input.67 npm8MIT
- FlicenseBqualityDmaintenanceA Model Context Protocol (MCP) server that provides US weather forecasts and active alerts using the National Weather Service API.2-