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SHEARLINE

de lostnumber07versión 1.1.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.lostnumber07/shearline

Instalar

Resumen

Descripción del creador (original en inglés)

Analyst-grade US severe weather: warnings, SPC outlooks, RAP environment, MRMS, GLM lightning

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Otros

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • PyPIuv de Python instalado (para el comando uvx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

{
  "mcpServers": {
    "shearline": {
      "command": "uvx",
      "args": [
        "shearline"
      ]
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add shearline -- uvx shearline

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "shearline": {
      "command": "uvx",
      "args": [
        "shearline"
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "shearline": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "shearline"
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub lostnumber07, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Otrosi

  • Más estrellas que el 0% de los otros 32583 servidores de esta categoría
  • Publicado en el registro más recientemente que el 13% de los otros 32583 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 32583 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro SHEARLINE?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (1.1.0) llegó al registro hace 5 meses.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Spec Driven Development, Remote Desktop Commander, sync. labs

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Spec Driven Development

de formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Se ejecuta en tu computadora★ 440MITActualizado hace 14 meses
Instalar

Remote Desktop Commander

de desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Se ejecuta en la nube★ 211Actualizado ayer
Instalar

sync. labs

de synchronicity-labs

Lipsync videos, dub them into other languages, make talking videos and open sync. Studio.

Computadora o nubeActualizado hoy
Instalar

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

SHEARLINE

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.

Tools

ToolWhat 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.

Example: threat brief during a real outbreak

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.

Install

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

serves at http://127.0.0.1:8741/mcp

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 varDefaultEffect
SHEARLINE_RATE_RPM60sustained requests/minute/client (0 disables the limit)
SHEARLINE_RATE_BURST30token-bucket capacity (max burst)
SHEARLINE_HTTP_LOG1set 0 to silence per-request logging
SHEARLINE_LOG_LEVELINFOlog level for the shearline.http logger
SHEARLINE_UPSTREAM_CONCURRENCY8max 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.

Why these tools

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:

  • Warnings with IBW tags, not just warning text. A base-tier Severe Thunderstorm Warning and one tagged 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.
  • The environment, computed honestly. CAPE without shear is a pulse-storm day; shear without CAPE is wind-driven rain. SHEARLINE pulls the current RAP analysis profile and computes the discriminating quantities with MetPy — including the effective inflow layer, effective SRH/shear, SCP, and STP — because high-CAPE/low-shear, low-CAPE/high-shear, and classic supercell parameter spaces produce very different hazards, and the interpretation says which one you're in.
  • MRMS, because warnings lag storms. MESH tells you what hail a storm has *already* produced; rotation tracks show where mesocyclones have tracked in the last hour — both on a ~2-minute cadence from the national radar mosaic, often ahead of the next warning update.
  • LSRs, because radar isn't ground truth. Spotter reports confirm what's actually reaching the ground — in real time, or for any past date (the insurance / forensic question, "what hit this address that day").
  • Lightning, because it's the most common outdoor-safety trigger. GOES GLM total lightning, distance-tiered to the 30-30 / 10-mile rules — the signal that actually stops a ballgame, a job site, or a drone flight.
  • **Now *and* next.** The point environment is the analysis now; the forecast *trend* (f00→f06 from one model cycle) says whether STP/CAPE is rising into the afternoon — the difference between "what is it" and "is it getting worse."
  • One brief that reasons across all of it. The threat level is rule-based with the triggered rules quoted back, so an agent can audit the logic instead of trusting a vibe.

Data sources (all public, no keys)

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.

Recipes for non-meteorologists

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:

  • [hail-claim-verification](.claude/skills/hail-claim-verification/SKILL.md) — did damaging hail occur at this address on this date? (insurance / forensic)
  • [chase-day-briefing](.claude/skills/chase-day-briefing/SKILL.md) — outlook → environment → trend → warnings → radar, into a go/no-go with a target

Solo se muestra el principio; lee el resto en el repo

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