Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Analyst-grade US severe weather: warnings, SPC outlooks, RAP environment, MRMS, GLM lightning
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"shearline": {
"command": "uvx",
"args": [
"shearline"
]
}
}
}ターミナルでこれを実行します
claude mcp add shearline -- uvx shearlineプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"shearline": {
"command": "uvx",
"args": [
"shearline"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"shearline": {
"type": "stdio",
"command": "uvx",
"args": [
"shearline"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント lostnumber07 で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(1.1.0)がレジストリに届いたのは5 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにPython (uv)も必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, Fomu MCP
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Kenya government form guides: checklists, draft letters, eCitizen, Huduma, timelines. Sample data.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
The severe-weather analyst your agent doesn't have. SHEARLINE is a free, MIT-licensed MCP server that gives AI agents analyst-grade US severe-weather tools: live warning polygons with Impact-Based Warning tags, SPC convective outlooks, RAP-derived point environments and forecast trends (CAPE/shear/SRH/STP computed with MetPy), MRMS radar-derived hail and rotation products, GOES lightning, ground-truth storm reports (real-time and historical), and a composite threat brief that synthesizes all of it. A dozen weather MCPs already wrap the basic forecast API; SHEARLINE deliberately skips everything they do and ships only what requires radar meteorology to expose correctly.
Informational only. Not a substitute for official NWS warnings. Every tool repeats this, because it matters: when weather threatens, follow official warnings from weather.gov and local authorities.
| Tool | What it returns |
|---|---|
get_active_warnings(lat, lon, radius_km=40) | Active tornado/severe-thunderstorm/flash-flood warning polygons with IBW tags (max hail size, max gust, tornado detection/damage threat), parsed storm motion, expirations, and whether the exact point is inside a polygon. Watches listed separately. |
get_spc_outlook(lat, lon, day=1) | SPC categorical risk (TSTM→HIGH) at the point plus tornado/hail/wind probabilities and significant-severe flags, days 1–3, with interpretation calibrated to the category. |
get_point_environment(lat, lon) | Latest RAP 13-km analysis profile computed with MetPy: MLCAPE/MUCAPE/CINs, LCL, 0–1/0–6 km shear, 0–1/0–3 km SRH, Bunkers motion, effective inflow layer, effective SRH/shear, SCP, and significant-tornado parameter — interpreted like an analyst (pulse vs. cool-season high-shear vs. classic supercell parameter space). |
get_environment_trend(lat, lon) | The anticipatory view: a short RAP forecast series (f00/f01/f03/f06, one consistent cycle) of MLCAPE, 0–6 km shear, 0–1 km SRH, SCP and STP, with an interpretation of the trajectory (intensifying / stabilizing / steady) — for "is this getting worse" rather than "what is it now." |
get_mrms_severe(lat, lon, radius_km=40) | MRMS maxima within radius: 60-min MESH (hail, inches and mm), low-level and mid-level rotation tracks (azimuthal shear), VIL, composite reflectivity — each with valid time and distance/bearing of the max. |
get_storm_reports(lat, lon, radius_km=80, hours=6) | Normalized Local Storm Reports: type, magnitude with units, time, location, distance/bearing, remarks. |
get_lightning(lat, lon, radius_km=40, minutes=15) | GOES-East GLM total-lightning activity in the recent window: flash count and rate, nearest strike (distance/bearing/time), and a tiered outdoor-safety interpretation (overhead / within-striking-distance / in-the-area). |
get_historical_storm_reports(lat, lon, date, radius_km=80) | What hail/wind/tornado hit a point on a specific past date (YYYY-MM-DD, UTC) — normalized reports with magnitude+units and distance/bearing, for the insurance / ag / forensic use case. Coverage from ~2005; preliminary LSRs, not the final NCEI record. |
get_threat_brief(lat, lon) | The showpiece: runs everything above concurrently and synthesizes a threat level (none/marginal/elevated/significant/extreme) with stated logic, hazards ranked, environment summary, nearest storm signature, and a recommended attention window. |
get_radar_snapshot(lat, lon) | Nearest WSR-88D's latest Level 2 volume metadata: VCP (scan strategy), max reflectivity with range/azimuth, coarse echo-top estimate. |
Every tool returns structured JSON with data (numeric fields, units stated), interpretation (plain-language analyst sentences), degraded (which upstream sources failed, if any — partial data instead of errors), the safety disclaimer, and a schema_version — the field contract is semver-stable (see ARCHITECTURE), so integrators can depend on it.
Real output from 2026-06-10, point inside an active tornado warning in northern Missouri:
{
"threat_level": "extreme", "threat_logic": [ "Tornado Warning in effect at the point, corroborated by confirmed tornado reports nearby — treat as an immediate life-safety situation.", "Severe Thunderstorm Warning at the point tagged 'Considerable' (hail to 1.75\", gusts to 60 mph).", "Significant-tornado parameter of 4.0 with storms ongoing — environment strongly supports tornadic supercells.", "MRMS MESH of 2.3\" hail within radius in the last hour.", "Intense rotation track (azimuthal shear 0.013 /s) nearby in the last hour.", "6 tornado report(s) near the point in the report window." ], "hazards_ranked": [ {"hazard": "tornado", "level": "extreme"}, {"hazard": "hail", "level": "extreme"}, {"hazard": "damaging_wind", "level": "extreme"}, {"hazard": "flash_flood", "level": "moderate"} ], "nearest_storm_signature": { "signature": "composite reflectivity", "value": "58.5 dBZ", "distance_km": 18.0, "direction": "ENE", "valid_utc": "2026-06-10T22:14Z" }, "attention_window": {"window": "now", "until_utc": "2026-06-10T21:00:00-05:00"} }
And the same tool for a quiet coastal Maine point reads as confidently quiet — not as an error: "threat_level": "none" with the environment numbers shown so the agent can see *why* it's quiet.
Requires Python 3.12+ and uv. No API keys — every data source is public and anonymous. uvx downloads and runs the published package in one step; nothing is installed permanently.
Claude Code:
claude mcp add shearline -- uvx shearline
Claude Desktop (claude_desktop_config.json):
{
"mcpServers": { "shearline": { "command": "uvx", "args": ["shearline"] } } }
Streamable HTTP (for remote/agent-platform use):
uvx shearline --http --port 8741
On macOS you can also double-click scripts/launch.command to start the HTTP server in a Terminal window (honours SHEARLINE_PORT).
The HTTP transport is built to be hosted: it emits one structured JSON log line per tool call (tool, coarse 1° lat/lon bucket, latency, degraded list, cache hit/miss) and applies a per-client token-bucket rate limit, returning 429 with Retry-After when exceeded. Both are HTTP-only — stdio behaviour is unchanged. Configure via environment variables:
| Env var | Default | Effect |
|---|---|---|
SHEARLINE_RATE_RPM | 60 | sustained requests/minute/client (0 disables the limit) |
SHEARLINE_RATE_BURST | 30 | token-bucket capacity (max burst) |
SHEARLINE_HTTP_LOG | 1 | set 0 to silence per-request logging |
SHEARLINE_LOG_LEVEL | INFO | log level for the shearline.http logger |
SHEARLINE_UPSTREAM_CONCURRENCY | 8 | max concurrent upstream fetches (politeness toward NOAA) |
To run the latest unreleased main instead of the PyPI release, swap shearline for --from git+https://github.com/lostnumber07/shearline shearline.
A forecast API tells you it might rain. None of the questions that matter on a severe weather day — *is this storm rotating, how big is the hail, is the environment loaded for tornadoes, am I inside the polygon* — are answerable from a forecast endpoint. They require the warning's IBW tags, radar-derived products, and a real sounding:
DESTRUCTIVE with 80 mph gusts are different planning problems. SHEARLINE parses the machine-readable tags (max hail size, max gust, tornado detection/damage threat) and the storm-motion vector, and does the point-in-polygon test for you.Coverage is continental US only — out-of-bounds coordinates are rejected with a clear error. Upstream fetches are cached (warnings 60 s, MRMS/lightning 120 s, LSRs 300 s, outlooks/RAP 30 min, historical reports 6 h) and degrade gracefully: if one source is down, you get partial data plus a degraded field, never a bare exception. A daily canary workflow checks every upstream's response shape and fails on schema drift (renamed fields, moved buckets) before it reaches you.
You don't need to know what an STP is to use SHEARLINE. The .claude/skills/ directory ships three end-to-end recipes that name the exact tool sequence for a domain task — drop them into any agent that has SHEARLINE connected:
冒頭だけを表示しています。続きはリポジトリで読めます