Weathersight

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 在云端运行
• 传输方式: Streamable HTTP
• 分类: 营销与社交媒体

安装

选择你使用的 AI 应用，然后复制设置。

你需要

• 在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

https://weathersight.io/api/mcp

claude mcp add --transport http weathersight https://weathersight.io/api/mcp

{
  "mcpServers": {
    "weathersight": {
      "url": "https://weathersight.io/api/mcp"
    }
  }
}

{
  "servers": {
    "weathersight": {
      "type": "http",
      "url": "https://weathersight.io/api/mcp"
    }
  }
}

https://weathersight.io/api/mcp

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月9日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• anom

  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 fo

• anomalies

  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

• bestplace

  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/

• besttime

  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_

• breaking

  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 for

• climatecomps

  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 c

• climatetrends

  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 av

• climatetwins

  Finds the places whose climate is most like a given location's. Each location has a climate fingerprint built from ERA5 reanalysis monthly climatology for 1996-2025: for each month, typical daily highs and lows, hot-day and cold-night extremes, dew point, rain and snow, plus elev

• compare

  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. Dat

• countries

  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. Prerequisite

• dailycomp

  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

• dailycountbyloc

  How often a compound daily weather condition happens, per location: for every location in an area, the average number of days per year that met all the criteria, with the count, the days that had data, and the percentage. When to use: Map where a condition is common or rare, e.g.

• dailycountbyyear

  For one location, the number of days per year that met all the criteria within a month-day window, for every year with data, plus a trend line (days per decade) with R squared and p-value. When to use: Answer 'is this condition becoming more frequent here?', e.g. hot summer days

• dailyfcasteval

  How good Weathersight's own forecast was at one location, at one or more lead times: a daily series of the observed value, the forecast made lead days earlier, and RMSE, MSE, ACC and ETS over a trailing window ending on each day. When to use: Answer 'how reliable has the 3-day fo

• dailyweather

  One day's weather across an area: every location matching the criteria on that date, with one or more metrics' values, anomalies (z) and percentiles. Observed days, or Weathersight's forecast for the next 7 days. The result is columnar: columns names each position, and every entr

• degreedays

  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 i

• events

  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

• extremes

  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; moder

• fcastevalbygroup

  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 th

• fcastevalbylocation

  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

• location

  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 po

• metrics

  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 sessi

• recentextremes

  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

• seasonal

  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

• timeseries

  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, n

• typicalweather

  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[

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月10日

• ✓ 通过

  作者与仓库一致

  注册名称已通过域名 weathersight.io 验证（注册中心会核实域名所有权）

• … 尚未检查

  仍在维护

  我们还没有从 GitHub 读取这个仓库

• … 尚未检查

  有许可证

  我们还没有从 GitHub 读取这个仓库

• ✓ 通过

  端点有响应

  在 355 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 26 个工具描述，未发现针对 AI 的隐藏指令

与“营销与社交媒体”中的其他服务器比较i

• 星标数多于此分类中其他 3717 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 3717 个服务器的 28% 更新
• 基础检查结果等于或优于此分类中其他 3717 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

作者的 README

https://weathersight.io/api/mcp