MCP server

Smarter Weather Official

io.github.smarterweather/weather

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Smarter Weather MCP: forecasts, alerts, outlooks, observations, AQI, grids, and map imagery.

Working at last checkOfficial: smarterweather.com's GitHub account (smarterweather)About 7,160 tokens per request once connected16 tools

Website Source

https://mcp.smarterweather.com

At a glance

Type: MCP server
Publisher: smarterweather.com (verified by the MCP Registry)
Status: Working at last check
Tools: 16
Token cost: About 7,160 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http weather https://mcp.smarterweather.com

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.weather]
url = "https://mcp.smarterweather.com"

Gemini CLI

{"mcpServers":{"weather":{"httpUrl":"https://mcp.smarterweather.com"}}}

Cursor

{"mcpServers":{"weather":{"url":"https://mcp.smarterweather.com"}}}

VS Code

{"servers":{"weather":{"type":"http","url":"https://mcp.smarterweather.com"}}}

Antigravity

agy mcp add weather https://mcp.smarterweather.com

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"weather","server_url":"https://mcp.smarterweather.com","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"weather","server_url":"https://mcp.smarterweather.com"}

Tools

call_tool ~157 tokens
Runs a tool find_tools lists, by name: same price, limits and result as calling it directly. Tools already in your list are called directly, not through this. `arguments` follows that tool's input schema.
describe_dataset ~162 tokens
A dataset's variables (standard names, units, display units, categories, bands), horizon, step, cadence, freshness, cost class and an example_query for query_dataset. Example: {"dataset_id": "nbm_conus"}.
find_tools ~221 tokens
Lists the weather tools this connection does not show (best times, comparisons, forecast totals, air quality; station observations, soundings, hurricane recon; lightning, storm cells, storm reports; outlooks, forecaster text, hurricanes; climate records and normals; forecast accuracy; maps and chart
get_alerts ~576 tokens
NWS watches, warnings, advisories. Point (city/ZIP/lat+lon): containing polygons. BBox or US state/DC/CONUS (codes, full names, US/national): intersecting polygons. A city miss is not a statewide all-clear — query the state or a bbox; never say regional inventory is impossible. NY/WA and "New York S
get_current_conditions ~503 tokens
Current weather right now at a location from two independent sources in one call: the RTMA gridded analysis (exact-point values, updated sub-hourly) and the nearest METAR station observation (ground truth with raw METAR, flight category). Use the analysis for point-accurate values and the station fo
get_forecast ~682 tokens
Complete weather overview for a location: current conditions, daily forecast (SPC threats, severity; view "detailed" adds CAPE, UV, full day/night periods), active alerts, and outlooks in one call. Pre-aggregated across NBM, HRRR, GFS, RTMA, and SPC and unit-converted server-side. Primary weather to
get_forecast_distribution ~768 tokens
NBM probabilistic guidance for one weather aspect: percentiles (p10-p90), exceedance probabilities and ensemble spread. Use this for any question about odds, ranges, potential or confidence ("how much could we get", "range of possible highs", "how sure is this") -- a deterministic forecast value can
get_hourly_forecast ~784 tokens
Blended hourly forecast: temperature, feels-like, humidity, wind, precipitation probability/amount, conditions, and icon per hour. hourly[0] is the current or next hour; hourly_coverage.first_time says which. Timestamps are UTC ISO 8601; convert to the local timezone before presenting. Ask for the d
get_observed_precipitation ~617 tokens
How much rain fell: observed precipitation totals at a location. Use for "did it rain", "how much rain fell", "rain in the last 24 hours / since yesterday". Source: MRMS MultiSensor QPE Pass2 (radar bias-corrected to rain gauges) over 1/3/6/24/48/72 h windows (CONUS; 1 h only in Alaska and Hawaii).
get_platform_status ~248 tokens
Current data-freshness status of the weather platform: overall state, per-source states (ok / degraded / outage / no_signal), open incidents with cause attribution (provider outage vs internal processing delay), and active provider advisories. Use this when a user asks whether data is current, when
get_time_context ~356 tokens
Complete temporal context for a location: local time, timezone, 14-day calendar with day names and Today/Tomorrow offsets, sunrise/sunset/solar times (from the weather pipeline's astro product), and moon phase. Use whenever you need to reason about dates, times, or daylight for a location -- includi
list_datasets ~372 tokens
Discover the gridded datasets (model grids, analyses) available at a location, or every public one when none is given, keyed by dataset_id. At a point: its domain (CONUS/Alaska/Hawaii) plus global grids, with freshness where tracked (latest model run as latest_run, latest_time, data_age_seconds, fre
query_dataset ~728 tokens
Raw time series of one dataset's variables at a point, with unit labels: any gridded product (NBM, HRRR, RRFS, GFS, RTMA, MRMS, air quality, ...). Time modes: hours (default 24), time_start+time_end (ISO 8601), or latest=true (newest value). Up to 168 frames: when truncated, send next_cursor as curs
reverse_geocode ~204 tokens
Resolve coordinates to a human-readable place (city, state, county, timezone). Use when you have lat/lon but need a display name or the local timezone. Example: {"lat": 39.74, "lon": -104.99} -> Denver, Colorado, America/Denver.
search_catalog ~427 tokens
Find which dataset answers a weather question when no dedicated tool fits: ranks the data catalog (model grids, radar, satellite, outlooks, observations) by the question's words and returns the best datasets with variable, units, cadence, a one-line why and the exact `call` (tool and arguments) to r
search_locations ~355 tokens
Resolve a place to ranked candidates with lat/lon: city ("Denver"), "city, ST", ZIP ("50219"), or partial input with fuzzy=true ("bost" -> Boston); venues and addresses where enabled. Rows carry `type` place|zip|address|poi; pass a row's lat/lon to weather tools. `matched_via: "autosuggest"` marks r

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