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Weather MCP Server

by rsys-vtiwari
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Weather MCP Server

An MCP server that provides:

  • Active US National Weather Service (NWS) alerts by two-letter state code

  • Short-term point forecasts (next 5 periods) for latitude/longitude coordinates

Data is sourced from the public NWS API at https://api.weather.gov.

Tools

Tool

Description

Inputs

get_alerts

Fetch active NWS alerts for a US state

state (string, two-letter code)

get_forecast

Fetch short-term forecast (next 5 periods) for coordinates

latitude (number), longitude (number)

Related MCP server: Weather Server

Usage (Local)

Install dependencies:

pip install .

Run the MCP server (stdio transport):

python -m weather.weather

Your MCP client should be configured to launch the server via the package entrypoint or the above module path.

Server JSON Summary

See server.json for registry metadata including name, version, tools, and entrypoint configuration.

Publishing Steps (Overview)

  1. Authenticate with publisher (GitHub namespace):

    mcp-publisher login github

  2. Create and push repo to GitHub (see steps below).

  3. (Optional) Publish to PyPI if distributing as a package.

  4. Publish to MCP registry:

    mcp-publisher publish

  5. Verify:

    curl "https://registry.modelcontextprotocol.io/v0/servers?search=io.github.vtiwari/weather-mcp"

Create & Push GitHub Repository

If this directory is not yet a git repo:

git init
git add .
git commit -m "Initial commit: Weather MCP server"

Create repo (GitHub CLI) and push:

gh repo create vtiwari/weather-mcp --public --source . --remote origin --push

If not using GitHub CLI, create the repo manually via the GitHub web UI, then:

git remote add origin https://github.com/vtiwari/weather-mcp.git
git branch -M main
git push -u origin main

Tag version for release consistency:

git tag v0.1.0
git push origin v0.1.0

PyPI Packaging (Optional)

To distribute via PyPI, ensure pyproject.toml includes build backend and metadata (authors, license). Then:

pip install build twine
python -m build
twine upload dist/*

License

MIT (adjust if different).

Disclaimer

This server uses public NOAA/NWS endpoints. Respect API usage guidelines and rate limits.

Available Tools

2 tools
get_alertsA

Get weather alerts for a US state.

Args: state: Two-letter US state code (e.g. CA, NY)

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters5/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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_forecastC

Get weather forecast for a location.

Args: latitude: Latitude of the location longitude: Longitude of the location

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes
longitudeYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.8/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters2/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • Changedget_alerts1 field changed
      • addedInput schema / title
        Added value: +"get_alertsArguments"
    • Changedget_forecast1 field changed
      • addedInput schema / title
        Added value: +"get_forecastArguments"
  2. 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

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_alerts retrieves weather alerts for US states, while get_forecast provides weather forecasts for geographic coordinates. There is no overlap in functionality or ambiguity between them.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. The naming is perfectly predictable across the tool set.

Tool Count2/5

With only 2 tools for a weather server, the surface feels thin and incomplete for the domain. A weather service typically requires more operations (e.g., current conditions, historical data, multiple forecast types) to be useful for agents.

Completeness2/5

There are significant gaps in coverage for a weather domain. Missing essential operations like current weather conditions, historical data, air quality, or radar information. The two tools alone do not provide a complete weather service surface.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

    BhavinPatel04
    A
    license
    Not graded
    quality
    D
    maintenance
    Provides access to National Weather Service (NWS) data, enabling users to retrieve active weather alerts for US states and weather forecasts for specific geographic coordinates.
    55 npm
    MIT

  • Weather MCP Server

    OleBo
    A
    license
    B
    quality
    D
    maintenance
    Provides weather information and alerts from the National Weather Service, including active weather alerts for US states and location-based forecasts using latitude/longitude coordinates.
    2
    MIT