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SHEARLINE

by lostnumber07version 1.1.0i★ –iRuns on your computer
Registry nameio.github.lostnumber07/shearline

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

claude mcp add shearline -- uvx shearline

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account lostnumber07, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32583 servers in this category
  • Published to the registry more recently than 13% of the other 32583 servers in this category
  • Basic checks equal to or better than 100% of the other 32583 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is SHEARLINE safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.1.0) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, sync. labs

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

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