Name: Weathersight
Author: weathersight

Weathersight MCP

Historical weather and climate: 100+ years of station data, normals, extremes, and trends.

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What it does

Historical weather and climate: 100+ years of station data, normals, extremes, and trends.

Quickstart

# 1 — add it to Claude Code
claude mcp add --scope user --transport http weathersight-mcp https://weathersight.io/api/mcp

# 2 — ask your agent for something these tools do
#   anom, anomalies, bestplace and 18 more

Weathersight costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.

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Tool surface

What the model actually sees. Descriptions are diffed on every release — see version history.

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Returns observations with 1991–2020 baseline comparisons at daily, weekly, or monthly granularity for a location and period. When to use: Graph or tabulate recent anomalies at a location for a specific period — e.g. 'how did last month compare to normal?' Date format: YYYYMMDD for ymd; use groupby (day/week/month) to control granularity. For 'last week' use YYYYMMDD of 7 days

Parameter	Type	Required	Notes
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport
observed_period	string	no	Observed period (default: 1)
period_type	string	no	Period Type [ MONTH \| YEAR \| WEEK \| DAY \| FULL_DAY \| FUTURE_DAY] (default: FULL_DAY)
ymd_end	string	yes	End of period (YYYY[MMDD])
ymd_start	string	yes	Start of period (YYYY[MMDD])

Returns precomputed anomalous observations for locations in a given time period and scope. When to use: Survey anomalous weather at regional or global scale quickly without specifying individual cities. Date format: YYYY[MM[DD]]: YYYY for annual anomalies (scope=year), YYYYMM for monthly (scope=month), YYYYMMDD for weekly or daily (scope=week/day). Performance: Fas

Parameter	Type	Required	Notes
anomaly_type	string	no	Anomaly Type [HIGH \| LOW]
category	string	no	Metrics Category[Not Available] as defined by /api/metrics
ctryid	string	no	Country IDs (comma sep) returned by /api/countries
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
metrics	string	no	One of metrics returned by /api/metrics. Overrides 'CAT'
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport
period_type	string	no	Period Type [MONTH \| WEEK \| YEAR \| DAY \| FULL_DAY \| FUTURE_DAY] (default: DAY)
scope	string	no	Scope Type [week \| month \| year]
ymd	string	yes	Time Period YYYY[MMDD]
z	string	no	Minimum avg std-devs departure from mean (default: 0)

Given a single week, month, or day, ranks locations whose weather best matches a set of criteria (most optional criteria met first; all MUST criteria satisfied by every result). Filter by lat/lon+radius or countries. When to use: Answer 'where has the best weather for week/month/day X?' Date format: scope=day + time=YYYYMMDD (forecast); scope=week + time=MMDD; scope=month + t

Parameter	Type	Required	Notes
baseline	string	no	Baseline year range YYYY-YYYY (default: 1996-2025)
criteria	string	yes	JSON array of criteria, passed as a string. Example: [{"metric":"avg:max_t.p50","lb":18,"ub":28,"must_have":true},{"metric":"avg:obs.rain.sum","ub":40,"must_have":true},{"metric":"avg:obs.is_rain.coun
ctryid	string	no	Comma-separated ISO country codes (mutually exclusive with latlon)
latlon	string	no	Latitude,Longitude for area search ex: 40.78,-73.97
max_results	string	no	Max results (default: 100)
radius_km	string	no	Radius km for area search (default: 100)
scope	string	no	day \| week \| month (default: week)
time	string	no	YYYYMMDD (day), MMDD (week), or MM (month)

Given a location, ranks the weeks or months whose historical climatology best matches a set of weather criteria (most optional criteria met first; all MUST criteria satisfied by every result). When to use: Answer 'when is the best time to visit X for weather like Y?' Choose when_type=week or month over the 1996-2025 baseline. Date format: when_type: week|month|day. Historical

Parameter	Type	Required	Notes
baseline	string	no	Baseline year range YYYY-YYYY (default: 1996-2025)
criteria	string	yes	JSON array of criteria, passed as a string. Example: [{"metric":"avg:max_t.p50","lb":18,"ub":28,"must_have":true},{"metric":"avg:obs.rain.sum","ub":40,"must_have":true},{"metric":"avg:obs.is_rain.coun
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
max_results	string	no	Max results (default: 25)
name	string	no	Location name, e.g. New York
when_basis	string	no	historical \| forecast (default: historical)
when_type	string	no	week \| month \| day (default: week)

Returns anomalous weather conditions (breaking news) across the world or specified countries in the very recent past (observations) or immediate future (forecast). When to use: First call when looking for current or upcoming notable weather events globally or by country. Date format: No date params — always returns the most recent ~3 days of observations (breaking_type=obs) o

Parameter	Type	Required	Notes
breaking_type	string	no	Type of anomaly source. Values: obs (recent observations, ~last 3 days), forecast (upcoming 0-7 days). (default: obs)
ctryid	string	no	Comma-separated country codes to filter (as returned by /api/countries). Leave empty for global results.
metric_category	string	no	Filter by metric category. Values: A (All), T (Temperature), P (Precipitation), H (Humidity/Dew Point), W (Wind). (default: A)

Returns monthly distributions of selected metrics for locations compared against a baseline period. When to use: Compute normalized and absolute deviations from a baseline, inter-annual and intra-annual variations within months and seasons. Check base_N and obs_N to assess data completeness. Date format: obs_years as YYYY or YYYY-YYYY range; months as MM or MM-MM (e.g. '06',

Parameter	Type	Required	Notes
base_years	string	no	Baseline period. Available: 1991-2020. (default: 1991-2020)
countries	string	no	Comma-separated ISO country codes to filter (e.g. 'GB,FR').
lat_lon	string	no	Latitude,Longitude of area centre (e.g. '51.5,-0.1'). Used with radius_km for area search. Ignored when locids is set.
locids	string	no	Comma-separated location IDs (as returned by /api/location). Example: '037720_99999,432950_99999'.
metrics	string	yes	Metric to compare. Available: max_t, max_t.means, max_wind, max_wind.means, min_t, min_t.means, obs.dewp, obs.dewp.means, obs.dpd, obs.dpd.means, obs.gust, obs.heatindex, obs.heatindex.means, obs.is_d
months	string	no	Single month ('06') or hyphenated range ('MM-MM'). Seasons: '12-02' (Dec-Feb), '12-03' (Dec-Mar), '10-11' (Oct-Nov), '03-05' (Mar-May), '06-08' (Jun-Aug), '06-09' (Jun-Sep). Use 'All' for annual. Exam
obs_years	string	no	Observation year or range (e.g. '2024' or '2020-2024'). Values can range from 2024 to current year, or a single hyphenated range e.g. 1981-2010.
radius_km	string	no	Search radius in km around lat_lon. Default 100. (default: 100)
src_type	string	no	Data source: Obs (station observations) \| ERA5 (reanalysis). (default: Obs)

Returns monthly timeseries of selected climate trend metrics for one or more locations, suitable for long-term trend analysis. When to use: Retrieve long-term monthly timeseries for statistical trend analysis across any number of locations including globally. Use num_years and avg_N to filter sparse records. Date format: years as YYYY- range (e.g. '1975-' for 1975 to present)

Parameter	Type	Required	Notes
avg_N	string	no	Minimum average number of days per period for years with data. (default: 0)
countries	string	no	Comma-separated ISO country codes to filter results (e.g. 'GB,FR').
lat_lon	string	no	Latitude,Longitude of area centre (e.g. '51.5,-0.1'). Used with radius_km for area search. Ignored when locids is set.
locids	string	no	Comma-separated list of location IDs (as returned by /api/location). Example: '037720_99999,432950_99999'. Use locids for point queries.
metrics	string	yes	Comma-separated metric(s) in format agg_func:metric_id.attribute. Example: avg:max_t.p50. Available: avg:max_t.mean, avg:max_t.p10, avg:max_t.p5, avg:max_t.p90, avg:max_t.p95, avg:max_wind.p95, avg:mi
months	string	no	Single month ('06') or hyphenated range ('03-05' for March-May, '06-08' for June-August) or 'All' for annual trends. Example: '06' or '03-05'.
num_years	string	no	Minimum number of years with non-null data. Use to filter sparse records. (default: 0)
radius_km	string	no	Search radius in km around lat_lon. Default 100. (default: 100)
src_type	string	no	Data source: Obs (station observations) \| ERA5 (reanalysis). ERA5 provides global coverage including ocean areas. (default: Obs)
years	string	no	Year range string. Known values: '1975-' (data from 1975 onward). (default: 1975-)

Compares a location's observed weather period against the corresponding periods in a historical baseline distribution. When to use: Quantify how extreme a specific period was across all metrics at once — e.g. how anomalous was March 2025 at New York vs the 1991–2020 baseline. Date format: YYYY[MM[DD]]: YYYYMMDD for a specific day or short window (period_type DAY/WEEK), YYYYMM

Parameter	Type	Required	Notes
baseline_offset	string	no	Baseline offset in years (default: 1)
baseline_period	string	no	Baseline period in years (default: 30)
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport
observed_period	string	no	Observed period (default: 1)
period_type	string	no	Period Type [ MONTH \| YEAR \| WEEK \| DAY \| FULL_DAY \| FUTURE_DAY] (default: DAY)
ymd_end	string	yes	End of period (YYYY[MMDD])
ymd_start	string	yes	Start of period (YYYY[MMDD])

Returns countries with their canonical country IDs used in all other API calls. When to use: Resolve country names to canonical IDs, or obtain the full list of supported countries. This list rarely changes. Performance: Fast — static data; safe to cache across calls. Prerequisites: None. Investigate: Call once if country-level filtering is needed; cache

Parameter	Type	Required	Notes
ctryid	string	no	Country Id

Returns per-day observed values with percentiles and z-scores vs a sliding N-day window over a historical baseline. When to use: Graph heatwaves, cold spells, and other streaks by showing daily departures from normal. Also compare two separate periods or locations using z-scores or percentiles for normalized severity. Date format: YYYYMMDD for ymd_start and ymd_end. Keep rang

Parameter	Type	Required	Notes
baseline_year_end	string	no	Baseline year end YYYY (default: 2020)
baseline_year_start	string	no	Baseline year start YYYY (default: 1991)
category	string	no	Category: Temp \| Prcp \| Wind \| Humidity (default: Temp)
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
name	string	no	Location name, e.g. New York
window	string	no	Sliding window days (3-7) (default: 7)
ymd_end	string	yes	Observation end date (YYYYMMDD)
ymd_start	string	yes	Observation start date (YYYYMMDD), max 5-year span

Returns cumulative degree-day indicators (heating/cooling/growing) for a location and period using ERA5 reanalysis data. When to use: Estimate energy demand anomalies or agricultural stress in a recent or upcoming period. Date format: YYYYMMDD for ymd_start and ymd_end. Maximum interval is 14 days per call. Performance: Computed on demand using ERA5; moderate speed

Parameter	Type	Required	Notes
base_temp_C	string	no	Base temperature in °C for all indicators (default: 18.3)
indicators	string	no	Comma separated list of indicator names. ['hdd'\|'cdd'\|'wbcdd'\|'gdd] (default: hdd,cdd,wbcdd)
is_forecast	string	no	Forecast requested? (0\|1) (default: 1)
latlon	string	yes	Latitude,Longitude ex: 40.78,-73.97
unit	string	no	Degree days as measured in F\|C (default: C)
ymd_end	string	no	End of period (YYYYMMDD)
ymd_start	string	no	Start of period (YYYYMMDD)

Finds days matching one or more compound weather conditions (e.g. hot + humid, rainy + windy) for a location. Returns up to 5000 most-recent matching events plus the total count. When to use: Count occurrences of compound weather events across years, study extreme or unusual day combinations, or identify streaks of such events. Date format: year_start/year_end as YYYY; month_

Parameter	Type	Required	Notes
baseline	string	no	Baseline period used to resolve sigma/percentile bounds, format YYYY_YYYY (default: 1991_2020)
criteria	string	yes	JSON array of criteria objects. Each criterion is either a numeric criterion {"metric":"<name>","bound_type":"A\|S\|P","lower_bound":<val>,"upper_bound":<val>,"must_have":true\|false} where bound_type A=
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
month_day_end	string	no	Month-day end filter MMDD e.g. 0831 (Aug 31). Omit for full year.
month_day_start	string	no	Month-day start filter MMDD e.g. 0601 (June 1). Omit for full year.
name	string	no	Location name, e.g. New York
year_end	string	no	Year end (YYYY, inclusive) (default: 2026)
year_start	string	no	Year start (YYYY) (default: 2024)

Returns extreme value analysis (return periods) for a specified location and metric. When to use: Quantify the statistical rarity of a record or near-record value. Typically called after other APIs establish that an observation is anomalous. Performance: Computed on demand; moderate speed. Prerequisites: Use /location to obtain a locid first. Prefer level='area' ov

Parameter	Type	Required	Notes
exceedance_type	string	no	Exceedance direction: High (probability of exceeding value) \| Low (probability of being below value). (default: High)
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
level	string	no	Aggregation level: city (single station) \| area (nearby stations pooled). Use 'area' to improve period of record. (default: area)
locid	string	no	Unique Location Identifier
metric_category	string	no	Metric category. Values: AVG_TEMP, DEWPH, DPD, MAX_TEMP, MIN_TEMP, PRCP, RAINH, SNOW, SNOWH, WBULB, WIND. Example: MAX_TEMP for maximum temperature extremes. (default: MAX_TEMP)
model_type	string	no	Statistical model: '' (empirical/default) \| MLE (maximum likelihood estimate).
month	string	no	Two-digit month filter (e.g. '06' for June). Leave empty for all months.
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport
season	string	no	Season filter as 'MM-MM' (e.g. '06-08' for Jun-Aug, '12-02' for Dec-Feb). Overridden by 'month' if both are set.
src_type	string	no	Data source: Obs (station observations) \| ERA5 (reanalysis). ERA5 recommended for precipitation outside US. (default: Obs)
window_size	string	no	Rolling window in days. Temperature metrics (MAX_TEMP, MIN_TEMP, AVG_TEMP, DEWPH, WBULB, DPD): 1, 2, 3. Precipitation metrics (PRCP, SNOW, RAINH, SNOWH): 1, 2, 3, 4, 7. (default: 1)

Scores how well each forecast model actually performed at each of many locations, by comparing what the models predicted against what was then observed. Returns one score matrix per location, models by evaluation metrics, for a single weather metric at a single lead time, plus the winning model per location. When to use: Answer 'which forecast model is most accurate here?' at scale — to

Parameter	Type	Required	Notes
ctryid	string	no	Comma-separated ISO country codes from /api/countries, e.g. US,CA. Omit for a global query. This endpoint compares many locations and has no single-location argument; for one place use fcastevalbyloca
eval_metrics	string	no	Evaluation metrics, comma-separated, any of: 'mae' mean absolute error in the metric's own unit, lower is better; 'rmse' root mean squared error, same unit, lower is better, more sensitive to large mi
lead_time	string	no	Forecast lead time in days. A lead time is how many days ahead the forecast was issued, so lead time 1 is yesterday's forecast for today and lead time 10 is a forecast made ten days before the day it
limit	string	no	Maximum locations returned, 1 to 4000 (default: 500)
metric	string	no	Metric names are literal and case-sensitive. Never drop the 'obs.' segment and never drop the '.mean' suffix. The eight metrics are: max_t.mean, min_t.mean, obs.temp.mean, obs.dewp.mean (C); obs.rain.
models	string	no	Forecast models, given as comma-separated keys: auto (Open-Meteo's own per-location blend — what a caller gets by default), IFS (ECMWF IFS 0.25 degree, physical NWP), GFS (NCEP GFS), AIFS (ECMWF AIFS,
sample_pct	string	no	Percent of the world's locations to sample, from 0.1 to 100. Sampling is by stable hash bucket, so every location has an equal chance of being selected and the sample is unbiased by geography. The gra
seed	string	no	Any string. The sample of locations is drawn deterministically from it, so the same seed always returns the same locations. Every response reports the seed it used, including when one was not supplied
threshold	string	no	Precipitation threshold in mm per day for the 'ets' eval metric, ignored otherwise. A day counts as a wet-day event when the total is at or above this. Useful rungs are 0.2, 1.0, 5.0, 10.0, 25.0. (def
ymd_end	string	no	Last day evaluated, YYYYMMDD, inclusive
ymd_start	string	no	Inclusive day range as YYYYMMDD. Defaults to the 14 days ending yesterday; today is never included because it has not been verified yet. At most 180 days, which is also how much history the index reta

Scores how well each forecast model performed at ONE location, across several forecast lead times and across a sliding time window. Shows both how fast accuracy decays as the forecast reaches further ahead, and how accuracy at a given lead time has varied over recent weeks. When to use: Answer 'how far ahead can I trust the forecast here?' or 'has the forecast been unusually poor here l

Parameter	Type	Required	Notes
eval_metrics	string	no	Evaluation metrics, comma-separated, any of: 'mae' mean absolute error in the metric's own unit, lower is better; 'rmse' root mean squared error, same unit, lower is better, more sensitive to large mi
latlon	string	no	'lat,lon' ex: 37.62,-122.4. Used if locid and name are absent
lead_times	string	no	Comma-separated forecast lead times in days. A lead time is how many days ahead the forecast was issued, so lead time 1 is yesterday's forecast for today and lead time 10 is a forecast made ten days b
locid	string	no	Location id from /api/location, e.g. 725030_14732
metric	string	no	Metric names are literal and case-sensitive. Never drop the 'obs.' segment and never drop the '.mean' suffix. The eight metrics are: max_t.mean, min_t.mean, obs.temp.mean, obs.dewp.mean (C); obs.rain.
models	string	no	Forecast models, given as comma-separated keys: auto (Open-Meteo's own per-location blend — what a caller gets by default), IFS (ECMWF IFS 0.25 degree, physical NWP), GFS (NCEP GFS), AIFS (ECMWF AIFS,
name	string	no	Place name, e.g. 'San Francisco'. Ignored if locid is set
threshold	string	no	Precipitation threshold in mm per day for the 'ets' eval metric, ignored otherwise. A day counts as a wet-day event when the total is at or above this. Useful rungs are 0.2, 1.0, 5.0, 10.0, 25.0. (def
window	string	no	Sliding window in days for the moving average. A 14 day range with a 7 day window yields 8 windows. Defaults to the whole range, which yields exactly one window
ymd_end	string	no	Last day evaluated, YYYYMMDD, inclusive
ymd_start	string	no	Inclusive day range as YYYYMMDD. Defaults to the 14 days ending yesterday; today is never included because it has not been verified yet. At most 180 days, which is also how much history the index reta

Returns meta information for a location given a name, locid, or lat/lon. When to use: Obtain the canonical locid needed by all location-specific API calls. Typically the first call in any workflow. Performance: Fast — precomputed lookup. Prerequisites: None — this is the entry point. Investigate: Call first whenever the user names a city or region; cache

Parameter	Type	Required	Notes
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport

Returns the catalogue of all supported weather metric identifiers with units and categories. When to use: Resolve metric IDs to human-friendly names, units, and categories. Call once and cache per session. Performance: Fast — static data; safe to cache for the lifetime of a session. Prerequisites: None. Investigate: Call once at session start; use metric

No parameter schema on file.

Returns recent weather records (all-time or period-of-record extremes) for the world or specified countries. When to use: Find which locations have broken or nearly broken records in a recent date window. Set history_years ≥ 15 to ensure a meaningful period of record. Date format: YYYYMMDD for ymd_start and ymd_end. Keep windows ≤ 3 months per call for reasonable performance.

Parameter	Type	Required	Notes
ctryid	string	no	Comma-separated country codes to filter (as returned by /api/countries).
history_years	string	no	Minimum years of history required to qualify as a record. Use >= 15 for meaningful records. (default: 5)
metrics	string	no	Applicable metrics. For period_type DAY: ('temp.max','temp.min', 'temp.mean', 'rain.mean', 'snow.mean', 'dewp.mean', 'wbulb.max', 'heatindex.max', 'wbgt.max', 'dpd.max','gust.max','wind.max','rainh.ma
period_type	string	no	Period type. Values: DAY (US daily obs), FULL_DAY (non-US daily obs), FUTURE_DAY (forecast). (default: FULL_DAY)
scope	string	no	Record scope. Values: month (monthly records), week (weekly records), year (annual records). (default: month)
ymd_end	string	no	End date YYYYMMDD. Defaults to today.
ymd_start	string	no	Start date YYYYMMDD. Defaults to 15 days ago.

Seasonal (monthly) forecast anomalies for the next six months, with the local climatology they are measured against. Each result gives, per metric: the ERA5 baseline mean (means), its interannual standard deviation (std), the predicted anomaly (anom, in degrees C / mm / km/h) and how unusual that is, both as standard deviations (norm) and as a percentile rank among the last 30 years (perc). <b

Parameter	Type	Required	Notes
baseline	string	no	Baseline year range YYYY-YYYY (default: 1996-2025)
criteria	string	no	JSON array of criteria, passed as a string. Optional for this API: omit it to get every location for the requested months. Example: [{"metric":"norm:max_t.mean","lb":1.5,"must_have":true},{"metric":"p
ctryid	string	no	Comma-separated ISO country codes (mutually exclusive with latlon)
cursor	string	no	next_cursor from a previous response. Only valid with sort=locid; sending it with a ranked sort is an error
fields	string	no	'full' for everything, 'compact' for locid/lat/lon/ym plus anom, norm and perc of the requested metrics only (default: full)
latlon	string	no	Centre of an area search as 'lat,lon' ex: 37.62,-122.4
limit	string	no	Results per page, 1 to 4000. 4000 covers every location in one page (default: 100)
locid	string	no	Location id from /api/location, e.g. 724940_23234
metrics	string	no	Comma-separated metrics to return: max_t.mean, min_t.mean, obs.temp.mean, obs.dewp.mean, obs.wind.mean, obs.rain.sum, obs.snow.sum. The metric named by sort is always returned as well, whether or not
name	string	no	Place name, e.g. 'San Francisco'. Ignored if locid is set
offset	string	no	Offset into a ranked result (any sort except locid). offset plus limit must be at most 4000. Use cursor, not offset, with sort=locid (default: 0)
radius_km	string	no	Radius km for latlon, 1 to 2000 (default: 100)
sort	string	no	One of: 'criteria' (most optional criteria met first), 'locid' (id order, resumable with cursor), or an attribute name to rank by how far from normal it is: 'norm:METRIC' or 'perc:METRIC', e.g. norm:o
src_type	string	no	Forecast model to read: SEAS5 (ECMWF seasonal). SEAS5 is currently the only value, so this can be omitted. (default: SEAS5)
ym_end	string	no	Last target month as YYYYMM, inclusive. Must be at least ym_start. Defaults to ym_start plus 5 months.
ym_start	string	no	First target month as YYYYMM, e.g. 202611. For a single month set ym_start and ym_end to the same value. Defaults to next month.

Returns aggregated metric values as a yearly or monthly timeseries for a location. When to use: Detect long-term trends and compute statistical significance (r-squared, p-value). Best suited for multi-decade spans. Date format: mm_dd_start/mm_dd_end as MMDD (day-of-year window, not a calendar year); year_start/year_end as YYYY. Note: Use mm_dd_start/mm_dd_end as MM when month

Parameter	Type	Required	Notes
groupby	string	no	Aggregating key: Year \| Month \| Week \| Hour (default: Year)
hour_end	string	no	Hour End (00 to 24) (default: 24)
hour_start	string	no	Hour Start (00 to 24) (default: 24)
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
metrics	string	yes	Comma separated list of tuple describing <i>aggregation function</i>:<i>metric_id</i>.<i>attribute</i> <br> ex: sum:obs.is_rain.count, avg:max_t.p99<br>agg_func = {sum,avg,min,max}. See /api/metrics f
month_day_end	string	no	Month day start MM[DD]
month_day_start	string	no	Month day start MM[DD]
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport
src_type	string	no	Data source type: Obs (station observations) \| ERA5 (reanalysis gridded data) (default: Obs)
year_end	string	no	Year end YYYY (default: 2025)
year_start	string	no	Year start start YYYY (default: 1975)

Returns statistical distributions of weather attributes (temperature, precipitation, wind, etc.) for a location and time period. When to use: Establish the climatological baseline — what is 'normal' for this location and period. Use before discussing anomalies. Date format: YYYY[MM[DD]]: YYYYMMDD for a specific week, YYYYMM for a month, YYYY for a seasonal or annual overview.

Parameter	Type	Required	Notes
hour_end	string	no	Hour End (00 to 24) (default: 24)
hour_start	string	no	Hour Start (00 to 24) (default: 24)
latlon	string	no	Latitude,Longitude ex: 40.78,-73.97
locid	string	no	Unique Location Identifier
lookback	string	no	Years looked back[1] (1 to 40) (default: 30)
name	string	no	Common name of metro or metro area. for ex: New York, Beijing, Mumbai Airport
ymd_end	string	yes	End of period (YYYY[MMDD])
ymd_start	string	yes	Start of period (YYYY[MMDD])

Free

$0 forever

Every tool — any call limit is its publisher’s, not ours
No card, and mcprush charges nothing for it
Runs at its publisher’s own address — this marketplace is not in the path
Nothing to cap, because mcprush bills nothing

Nothing here is billable: mcprush charges nothing to install Weathersight or to call it, at any volume.

ATool-list grade · score 96 / 100

Release 1.0.0 · scanned 20 Oct 2026

Graded from the tools this server lists, not from its source code.

Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.

Threat score100

Client score96

Findings0 threat · 0 capability

Capabilityminimal

Coveragemanifest

Verificationnot in this report

Surface digestced9ebab6356502f559e2649513ac5119deccec642a3d36c498ca30f54c7d165

Score vectorthreat 100 → client 96

Term	Level	What it prices	Points
capability-exposure	minimal	capability blast radius (minimal) — client exposure if the model is manipulated	−0
coverage-honesty	manifest	inspection depth (manifest) — how much of the target the scan could see	−4

score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.

What the scan could actually read

A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.

The scan read each tool’s name, description, input schema and annotations exactly as the server listed them the last time it answered us; no code was reviewed. We ask the server again about once a month, and a list that changed is graded again.

Tool surfaceread

Implementation sourcenot available

Package metadatanot available

Live transportnot available

The implementation source was not read — this reflects the declared tool metadata only.

Capability — what it could do if the model were manipulated

Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.

Tool	Capability tags	Why the tag was assigned
anom	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
anomalies	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
bestplace	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
besttime	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
breaking	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
climatecomps	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
climatetrends	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
compare	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
countries	untrusted-input	untrusted-input: keyword "fetch", declares openWorldHint: true (interacts with entities outside the server)
dailycomp	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
degreedays	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
events	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
extremes	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
fcastevalbygroup	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
fcastevalbylocation	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
location	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
metrics	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
recentextremes	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
seasonal	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
timeseries	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)
typicalweather	untrusted-input	untrusted-input: declares openWorldHint: true (interacts with entities outside the server)

Toxic-flow graph

The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.

The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.

Supply chain and provenance

Integrityfirst-seen

Surface digestced9ebab6356502f

Scanned2026-10-20

This is the first scan of this surface here, so there is nothing yet to compare it against.

Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.

Where are you running it?

Tool surface

Release history

Weathersight MCP

What it does

Quickstart

Where are you running it?

Tool surface

What counts against your monthly calls

What the scan could actually read

Capability — what it could do if the model were manipulated

Toxic-flow graph

Supply chain and provenance

Release history

See also