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gifflet

Weather MCP

by gifflet
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Weather MCP 🌤️

A modern weather service built with MCP (Multi-Cloud Platform) that provides real-time weather data and alerts.

🌟 Features

  • 🌡️ Real-time weather forecasts

  • ⚠️ Weather alerts by state

  • 📍 Location-based weather information

  • 🔄 Easy-to-use API endpoints

Related MCP server: Weather MCP Server

🚀 Getting Started

Prerequisites

  • Node.js (v18 or higher)

  • MCP Server

  • Git

Installation

  1. Clone the repository:

git clone https://github.com/gifflet/weather-mcp.git
cd weather-mcp

  1. Install dependencies and build the project:

npm install && npm run build

💻 Local Development with MCP Server

Configuring MCP Server

  1. Create a .cursor/mcp.json file in your project directory with the following content:

{
  "mcpServers": {
    "weather-service": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js"],
    }
  }
}

Where /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js is the path to the index.js file in the build folder of the weather-mcp project.

Alternatively, for global configuration, you can create the file at the root of your home directory: .cursor/mcp.json.

Starting the MCP Server

  1. Open the project in Cursor IDE

  2. Go to Cursor Settings > Features > MCP

  3. Your weather service should appear in the list of available MCP servers

  4. If needed, click the refresh button in the top right corner to populate the tool list

Using the Weather Service

After configuring and starting the MCP server in Cursor, you can interact with the weather service using natural language queries. Here are some examples:

Example Queries

  • "What's the weather in Sacramento?"

  • "Are there any active weather alerts in Texas?"

  • "What's the forecast for San Francisco?"

  • "Show me weather alerts for CA"

Note: Since this service uses the US National Weather Service API, queries will only work for locations within the United States.

Under the Hood

When you make a query:

  1. Your question is sent to the LLM

  2. The LLM analyzes the available tools and decides which one(s) to use

  3. The client executes the chosen tool(s) through the MCP server

  4. The results are sent back to the LLM

  5. A natural language response is formulated and displayed to you

Troubleshooting Common Issues

If the tools are not working as expected:

  • Verify your server builds and runs without errors

  • Check that the path in your .cursor/mcp.json is correct and absolute

  • Restart Cursor IDE if needed

  • For coordinates outside the US, you'll receive an error as the service only supports US locations

  • During high traffic, the weather service API might have rate limits

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

2 tools
get-alertsC

Get weather alerts for a state

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYesTwo-letter state code (e.g. CA, NY)

TDQS

C2.9/5.0
Behavior2/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYesLatitude of the location
longitudeYesLongitude of the location

TDQS

C2.9/5.0
Behavior2/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

  1. 2 tool updates
    • First observedget-alerts
    • First observedget-forecast
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

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one retrieves weather alerts for states, while the other provides forecasts for locations. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the need.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'get-' prefix and hyphenated names (get-alerts, get-forecast). This uniformity makes the tool set predictable and easy to understand.

Tool Count2/5

With only two tools, the server feels under-scoped for a weather domain. Key operations like getting current conditions, historical data, or radar imagery are missing, making the set too thin for comprehensive weather-related tasks.

Completeness2/5

The tool set is severely incomplete for a weather server. It lacks basic operations such as current weather, historical data, or severe weather details, creating significant gaps that will hinder agents in performing common weather-related workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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