Baseline Mcp

io.github.elninyo-ops由 GitHub 账号 elninyo-ops 发布v0.1.9更新于 Oct 24, 2026

Statistically rigorous weather and climate context: forecast plus 35-year historical ranking.

已验证STDIO仅桌面位置与生活数据与分析

概览

AI 生成的概览

本地 MCP 服务器,为助手提供任意地点按 35 年记录排序的天气与气候背景,并附站点交叉核对和明确的可信度说明

可以做什么
用自然语言提问,获得预报加上 35 年排序的气候背景;也可按精确经纬度查询,或查看某地自水文年开始的降水与气温在 35 年中的排名。比较某地近期天气相对常年有多异常。在 2 至 10 个地点或某个精选类别之间,按水文年、季节、月份或滑雪季对降水、气温或降雪进行排名。每条回复都带有评估字段,说明提供方自身的置信度,包括接近并列、站点不一致以及未能进行交叉核对的情况。
适用场景
当问题需要历史气候排名而非单一数字时适用,例如某月是否异常偏暖或偏湿、某季节是否接近常年,或若干地点在一个季节或水文年内的比较。也适合需要站点交叉核对和诚实置信度说明的争议或规划场景。
运行要求
以 Python 包(pip 或 uvx)形式通过 stdio 在本地运行,或作为可流式 HTTP 服务器运行在 127.0.0.1。需要环境变量 BASELINE_API_URL 和密钥 BASELINE_API_KEY;密钥不开放自助申请,需通过邮件索取。需要访问 Baseline API 的网络连接。
安装前请注意
必须提供 BASELINE_API_KEY,每次调用都计入该密钥的配额;HTTP 模式下每个请求都须在 X-Api-Key 或 Authorization 头中携带调用方密钥,无有效密钥的请求返回 401。查询内容和坐标会发送给第三方 Baseline API。站点交叉核对仅覆盖美国,季节展望仅提供降水。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Baseline Mcp,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

Baseline Climate

Stop your agent inventing climate context. Ranked 35-year records for any point on Earth, cross-checked against real stations, with an explicit reliability note to relay instead of guess.


Ask it something, and this is what comes back.

A ranking — with the near-tie disclosed, and a station agreeing:

August 2026: Mean temperature was 75°F, +2.8°F from normal, ranking as the 8th warmest since 1991 — though the 7th warmest through 9th warmest years are within 0.7°F of each other. A nearby station (CASPER-NATRONA COUNTY AP, 8.4 mi) agrees: +2.1°F from its own normal, against the reanalysis's +2.8°F.

A station that does not agree — the gauge a mile away read half the grid's total:

March 2026: Precipitation totaled 2.30 in — near average for this period historically. A nearby station (BOZEMAN MONTANA STATE UNIVERSITY, 1.3 mi) measured 1.12 in, against the reanalysis's 2.30 in.

A wind ranking that says plainly it is modeled, not measured:

March 2026: Modeled peak gust: 60 mph, ranking as the 3rd gustiest since 1991 — though it's within 1 mph of the 6th gustiest year. A station (Saratoga, 32.1 mi) measured a peak gust of 59 mph. The reanalysis figure is a modeled estimate, not a direct reading.

A seasonal outlook carrying its own skill label:

Leaning wetter than normal for Sep–Nov in Nairobi, on odds we haven't been able to verify here — below normal 5%, near normal 19%, above normal 76%. Forecasts here do track wetter and drier seasons, so the direction is the part worth using; whether the percentages themselves beat a climatological guess has not been shown.

Four things worth noticing. It gives you the rank, not just the number. It tells you when the rank is a near-tie, so you don't over-read it. It goes and asks a real weather station whether the gridded record is right — and when the station disagrees, it says so rather than papering over it. And when it can't vouch for its own forecast, it tells you that too.

The data isn't new. Asking it a question is.

ERA5 has been sitting in public archives for years. Interrogating it meant knowing which dataset, writing the code, and having an afternoon. That cost is what collapsed — and the interesting consequence is that people now ask things they'd never have thought worth the trouble.

The Bozeman example above isn't a weather lookup. It's a construction schedule dispute, where the question was whether rain actually stopped work — and the gridded record said one thing while the gauge a mile away said another. The wind example is a trucking corridor. Neither would have been worth opening a climate dataset for. Both take a sentence now.

That's the claim: not that historical weather data exists, but that the cost of asking it a real question has fallen far enough that weather becomes one ordinary input into questions that aren't about weather.

What it won't claim

Most tools hand you a number. This one hands you the number and its standing.

  • Near-ties are disclosed. "8th warmest — though the 7th through 9th are within 0.7°F" stops a model reporting a rank as though it were a meaningful gap.
  • Station disagreement moves confidence, and says which way. A gauge reading 1.12 in against a grid's 2.30 in is reported, not averaged away.
  • When no cross-check ran, it says why — no nearby station, outside the covered region, source didn't respond. The gap is named rather than left for the model to fill.
  • Seasonal outlooks carry an honest skill label. Calibrated from ECMWF SEAS5 against 35 years of observations, then labelled by what testing actually showed: that the odds beat a climatological guess here, or that only the direction is worth using, or — where a simple warming trend predicted the season better than the model did — that the model isn't the thing to go on. We publish the cases where our own forecast isn't the best available answer.

Every response carries an assessment field with Baseline Climate's own confidence statement, and the tool descriptions instruct the model to relay it rather than compose its own. That exists because the failure mode is real: given a bare number, models invent records that weren't broken, assert a dataset is unreliable somewhere it isn't, and recycle one station disagreement across unrelated answers.

What it's made of

Global daily recordERA5-Land reanalysis, 0.1°, 1991–present, land-only
Station cross-checkACIS (NOAA RCC). United States only — outside the US, answers say plainly that no station check ran
Seasonal outlookECMWF SEAS5, calibrated per region. Precipitation only — temperature has not yet cleared our skill test, and we don't issue one until it does
ForecastOpen-Meteo

Rankings use the full 1991–present record; "normal" means the WMO 1991–2020 reference period. See METHODOLOGY.md for how rankings, percentiles and water years are computed, and where the limits are.

Tools

  • get_climate_context — a natural-language question; forecast plus 35-year ranked context.
  • get_context_for_coordinates — the same for an exact lat/lon.
  • get_water_year_status — precipitation and temperature since the start of the water year, ranked against 35 years.
  • compare_to_normal — how unusual near-term conditions are at one location.
  • compare_locations — rank precipitation, temperature or snowfall across 2–10 locations, or a curated category, over a water year, season, month or ski season.

Install

Requires an API key. Self-serve signup isn't available yet — email Chad McNutt ([email protected]) during this early period.

bash
pip install baseline-mcp     # or: uvx baseline-mcp

Claude Code

bash
claude mcp add baseline \  --env BASELINE_API_URL=https://api.baselinecontext.com \  --env BASELINE_API_KEY=your-key-here \  -- uvx baseline-mcp

Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json:

json
{  "mcpServers": {    "baseline": {      "command": "uvx",      "args": ["baseline-mcp"],      "env": {        "BASELINE_API_URL": "https://api.baselinecontext.com",        "BASELINE_API_KEY": "your-key-here"      }    }  }}

Cursor — .cursor/mcp.json, same shape as above.

As an HTTP server (streamable HTTP, for hosting behind a reverse proxy):

bash
BASELINE_API_URL=https://api.baselinecontext.com baseline-mcp --transport http --port 8765

It serves /mcp on 127.0.0.1 and holds no key of its own: every request must carry the caller's key in an X-Api-Key (or Authorization: Bearer) header, and each tool call is made with that key, so it counts against that key's own quota. A request without a valid key gets a 401. The default, --transport stdio, is the local server shown above.

Contributing

This package is a thin translation layer: no climate logic lives here, and every tool call is an HTTP request to the Baseline Climate API. See CONTRIBUTING.md in the repository for local development setup.

MIT licensed.

来源:README.md,提交 379d7cc

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

3
  1. v0.1.9最新Sep 26, 2026
  2. v0.1.8Sep 22, 2026
  3. v0.1.1Sep 16, 2026

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