Weather MCP Server
Allows users to interact with the weather MCP server via Slack, enabling natural language requests for weather alerts and forecasts directly in Slack channels or DMs.
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 Serverany weather alerts in 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.
Weather MCP Server
A Model Context Protocol (MCP) server that provides real-time US weather alerts and forecasts by integrating with the National Weather Service API. It connects directly to Claude Desktop, enabling Claude to fetch live weather data through natural language requests.
Demo

Related MCP server: Weather MCP Server
Tools
get_alerts— Get active weather alerts for a US state (e.g.TX,CA)get_forecast— Get weather forecast for a location by latitude and longitudeget_hourly_forecast— Get hour-by-hour forecast for the next 12 hours by latitude and longitude
Setup
Install uv
Clone the repo and run
uv syncAdd to your
claude_desktop_config.json:
{
"mcpServers": {
"weather": {
"command": "uv",
"args": ["--directory", "/path/to/weather", "run", "weather.py"]
}
}
}Slack Chatbot Setup
Use this if you want Claude + weather tools inside Slack.
In your Slack app, ensure these are enabled:
Bot scopes:
app_mentions:read,chat:write,channels:history,im:historyEvents:
app_mention,message.imSocket Mode ON with an app-level token (
connections:write)
Create env file from template:
cp .env.example .envFill
.envwith your real values:
SLACK_BOT_TOKEN(xoxb...)SLACK_APP_TOKEN(xapp...)SLACK_SIGNING_SECRETANTHROPIC_API_KEY
Install dependencies and run:
uv sync
uv run slack_bot.pyIn Slack:
Mention the bot in a channel:
@MCP-NWS weather alerts for TXOr DM the bot:
forecast for 37.7749, -122.4194
Available Tools
3 toolsget_alertsB
Get weather alerts for a US state.
Args:
state: Two-letter US state code (e.g. CA, NY)
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 doesn't mention return format, pagination, whether alerts are current or historical, severity levels, or any rate limits or auth requirements. For a read-type tool the lack of annotations leaves significant gaps.
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?
Extremely concise and well-scoped. The description is two lines with an args docstring for the single parameter. Every word is functional with zero waste.
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?
An output schema exists, which reduces the burden for explaining return values. However, with 0% schema description coverage, 1 single param (already covered in the args), no annotations, and no behavioral details (alert types, severity, expiration, update frequency), the description is minimal. For a weather alert tool, agents would benefit from knowing what constitutes an alert return vs. a no-alert return.
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 single parameter 'state' is described in the argument docstring as a two-letter US state code with examples (CA, NY), which adds meaning beyond the bare schema. However, it doesn't specify case sensitivity, whether territories are included (e.g., DC, PR), or what happens for invalid state codes. The examples help but full semantics aren't covered.
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 gets weather alerts for a US state, with a specific verb ('get') and resource ('weather alerts') and a geographic scope ('US state'). It distinguishes from the sibling tool 'get_forecast' by the resource type, though it doesn't explicitly name the sibling.
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 implies usage context ('for a US state') but provides no when-to-use guidance, no exclusions, and no comparison to the sibling get_forecast tool. The intent is reasonably clear but the agent doesn't know when alerts vs forecast is more appropriate.
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
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 doesn't disclose how many days of forecast are returned, data freshness, caching behavior, rate limits, or what units (Celsius/Fahrenheit) are used. The description only restates the parameters without adding 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 brief but under-specified rather than appropriately concise. The Args section is mostly unnecessary given the input schema repeats the same parameter names and types. The single opening sentence is useful, but the parameter documentation is redundant with the schema.
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?
There is an output schema present, which helps, but the description still fails to communicate what the forecast contains or how comprehensive it is. For a tool that returns weather data, the agent has no sense of forecast length, granularity, or data fields without inspecting the output schema. The description is minimally sufficient for a basic 2-param lookup tool but leaves key context undocumented.
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?
Schema description coverage is 0%, meaning the description must compensate. The description merely restates that latitude/longitude are coordinates of the location, which adds marginal value over the schema. It doesn't explain valid ranges (e.g., -90 to 90, -180 to 180), precision requirements, or format expectations. Minimal semantic addition.
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 states 'Get weather forecast for a location' with a clear verb+resource. It doesn't distinguish from its sibling tool get_alerts, and 'forecast' vs 'alerts' distinction is implied but not explicit. The purpose is clear but missing scope details (time range, units).
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 vs get_alerts. The description doesn't explain the distinction between getting a forecast and getting alerts, nor does it mention any special circumstances (e.g., use get_alerts for severe weather warnings). No when/when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hourly_forecastB
Get hourly weather forecast for a location.
Args:
latitude: Latitude of the location
longitude: Longitude of the location
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only implies a read-only operation via 'Get'. It does not disclose forecast duration, units, timezone, or other behavioral traits, though it is not misleading.
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 brief and front-loaded with the primary verb. The Args list is somewhat redundant with the input schema but keeps the information organized and scannable.
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 two-parameter tool, the description provides basic invocation details, but it omits the forecast period, units, or how it differs from get_forecast. The presence of an output schema reduces some burden, but the usage context remains thin.
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?
Schema description coverage is 0%, so the description compensates by listing latitude and longitude, but only with generic restatements of the parameter titles. It adds no range, unit, or format details beyond 'of the location'.
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 retrieves hourly weather forecast for a location, using a specific verb and resource. The word 'hourly' helps distinguish it from get_forecast and get_alerts, though it does not explicitly name the alternatives.
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 is given on when to use this tool versus get_forecast or get_alerts. There are no prerequisites, exclusions, or contextual scenarios provided, leaving the agent to infer usage from the name alone.
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.
3 tool updates
v0.1.0- First observed
get_alerts - First observed
get_forecast - First observed
get_hourly_forecast
TDQS
Scored across 3 tools
get_alerts is clearly distinct, while get_forecast and get_hourly_forecast overlap in inputs but differ in time granularity. The descriptions make the distinction clear enough for an agent.
All tools follow the consistent verb_noun pattern 'get_<resource>' with clear resource names (alerts, forecast, hourly_forecast). No naming irregularities.
Three tools is slightly thin for a weather domain, but each serves a core need (alerts, daily forecast, hourly forecast). The set is well-scoped and not bloated.
Covers alerts and forecast types, but lacks current conditions or historical data. The main forecast lifecycle is covered, with minor gaps an agent could work around.
Maintenance
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