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Blast Scope

by Atharva-Jayappaversion 0.6.1i★ –iRuns on your computer
Registry nameio.github.Atharva-Jayappa/blast-scope

Install

Overview

Description from the maker (original English)

Contextual blast-radius scoring for shell commands an AI agent is about to run

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": {
    "blast-scope": {
      "command": "uvx",
      "args": [
        "blast-scope"
      ]
    }
  }
}

Run this in your terminal

claude mcp add blast-scope -- uvx blast-scope

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "blast-scope": {
      "command": "uvx",
      "args": [
        "blast-scope"
      ]
    }
  }
}

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": {
    "blast-scope": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "blast-scope"
      ]
    }
  }
}

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 Atharva-Jayappa, 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 32599 servers in this category
  • Published to the registry more recently than 22% of the other 32599 servers in this category
  • Basic checks equal to or better than 100% of the other 32599 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 Blast Scope 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 (0.6.1) reached the registry 3 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, Zendesk

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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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.

Blast Scope

A consequence engine for shell commands. Blast Scope scores what a command would actually *do* — before an AI agent (or you) runs it. It doesn't pattern-match syntax into a blocklist; it figures out the command's real target, observes that target with a safe, read-only probe, and returns a structured risk score with evidence.

The whole point is *contextual* blast radius. The same command gets a completely different score depending on what it would actually hit:

COMMAND                            SEVERITY   WHY                                          ADVICE

───────────────────────────────── ──────── ────────────────────────────────────────── ─────── rm -rf ./logs LOW 0 importers · regenerable · outside src proceed rm -rf ./config CRITICAL 8 modules import it · high PageRank hub block git reset --hard (clean tree) LOW nothing uncommitted to discard proceed git reset --hard (4 dirty files) HIGH would throw away 4 files of uncommitted work confirm git push --force (protected) CRITICAL would orphan commits on a protected branch block docker volume rm cache (absent) LOW volume doesn't exist — nothing to remove proceed docker volume rm pgdata (in use) CRITICAL holds data · in use · no image to rebuild block pip uninstall flask (uv.lock) LOW regenerable — exact version pinned in lock proceed DROP TABLE users (42 rows) CRITICAL schema + 42 rows · irreversible block DELETE FROM logs (in txn) HIGH no WHERE — but inside a txn, ROLLBACK-able confirm

Two commands can be byte-identical and score four bands apart. That gap is the product.

Not a blocklist. Not a replacement for Shellfirm. Not a syscall monitor. It

scores *structural consequence* — advisory, never blocking — and for the rare

critical command it captures an undo snapshot first.


How it works

A command flows through a cheap funnel: almost everything is recognized as non-destructive in microseconds and exits silent. Only a flagged *destructive candidate* pays for a probe.

  shell command

│ split chains (&& || ; |) · de-alias PowerShell · parse flags/targets ▼ ┌──────────────────────────────────────────────────────────────────────┐ │ STAGE 1 · triage (near-free regex — runs on every command) │ │ which class? git · docker · pip/uv · sql · else filesystem │ │ destructive? git status → no. git reset --hard → yes ↓ │ └───────────────────────────────┬──────────────────────────────────────┘ destructive candidate │ (everything else exits here, silent) ▼ ┌──────────────────────────────────────────────────────────────────────┐ │ ELIGIBILITY FILTER safe read-only probe? AND undo authorable? │ └──────────────┬──────────────────────────────────────┬─────────────────┘ yes, probe it │ no probe here / now │ ▼ ▼ STAGE 2 · safe probe (read-only) heuristic estimate git status · reflog · rev-list from a static per-class docker inspect · ps · ls table — and LABELED sqlite SELECT count(*) [mode=ro] "(estimated)" so you pip/uv read lockfiles know it wasn't probed │ │ └─────────────────────┬─────────────────────┘ ▼ blast radius × reversibility (combined PER CLASS — no global formula) filesystem also folds in: dependency-graph centrality + recoverability ▼ score 0.0–1.0 → severity (low / medium / high / critical) ▼ PreToolUse hook: silent (low/med) · advise (high) · advise + snapshot (critical)

The eligibility filter is the design boundary. A command class earns a *live probe* only when both hold: (1) its impact is observable by a strictly side-effect-free read (HTTP-GET sense — never mutate state to assess state), and (2) its undo story is well-known enough to encode in a static table. When a probe can't run here and now (no docker daemon, no DB driver, no creds), the tool degrades to a labeled estimate — it never guesses silently, and it never blocks.

See docs/heuristics.md for the per-class tables, the exact filesystem formula, and calibration.

The five command classes
ClassDestructive ops it scoresSafe (read-only) probeReversibility signal
Filesystemrm -rf, mv, > truncatedependency graph + git statusgit-tracked? regenerable? secret? precious?
Gitreset --hard, push --force, branch -D, clean -fdxstatus · reflog · rev-list · rev-parse @{u}reflog window · remote ahead · protected branch
Dockervolume rm, system prune -a, rm -fvolume inspect · ps -a · volume lsvolume → none · container → recreatable from image
pip / uvpip uninstall, uv pip uninstallread lockfile / manifest (no subprocess)lockfile present → fully regenerable
SQLDROP, TRUNCATE, DELETE without WHERESQLite: SELECT count(*) mode=ro; transaction checkinside a transaction? backup posture?

New classes drop in behind one protocol (triage / assess) in src/blast_scope/classes/; each class confines assess to strictly side-effect-free reads.


Status

Calibrated multi-class guardrail with command resolution and a precise dependency graph.

CapabilityModule
Flag/operand-sensitive command model (POSIX and PowerShell)command_effects.py, command_parser.py
Command resolution — env/tilde/brace/glob expansion, unset-var hazards, script transparency (sh -c, npm run + pre/post hooks, script files, Makefile targets), read-only $(...) substitutionresolution.py
Dry-run oracles — git clean -n exact lists, reset divergence, checkout clobber preview, find -delete→-print rewrite, sqlite scoped-DELETE counts, rsync --dry-run; oracle targets feed the undo snapshotclasses/git.py, classes/find.py, classes/rsync.py, classes/sql.py
Recoverability classification (git state, secrets, regenerable, precious data)recoverability.py
Dependency graph + weighted PageRank centrality, incremental indexinggraph_resolver.py, centrality.py
Two-axis, evidence-based filesystem scoringrisk_scorer.py
Command-class probes — git / docker / pip·uv / SQL, behind one protocolclasses/
Out-of-graph path analyzers (infra / config-by-path) + git baseconsequences.py, vcs.py, infra.py, config_refs.py
PreToolUse hook + tarball snapshot/undohook.py, snapshot.py
Eval harness + labeled corpus + calibrationeval.py, tests/fixtures/eval_corpus.jsonl

Calibration. Two harnesses, both run-it-yourself:

  • In-repo corpus (tests/fixtures/eval_corpus.jsonl, 58 cases spanning every recoverability category, git working-tree state, infra/config, rm -rf .git, a graph-indexed central module, the git/docker/pip/SQL classes, and the resolution layer — unset-var collapses, glob/env-var targets, sh -c payloads, npm pre-hooks, opaque wrappers, mass destruction of tracked source) — 58/58 exact severity, gate F1 1.00, pinned by tests/test_eval.py with headroom so changes can't silently regress.
  • [SABER](https://github.com/sssr-lab/saber) — 716 real coding-agent workspaces. Against ~1725 safe commands, blast-scope's false-positive rate is 0.58%; on its core competency (data_destruction) it catches 82.4% of injected attacks on realistic workspaces — on the fast hook path, no graph required, thanks to command resolution (env/glob binding + script transparency). Wrapper transparency also lifts code_tampering from ~0% to 50%. The per-category recall is deliberately uneven, and the table says so: blast-scope scores *destructive consequence* — filesystem/data loss plus git/docker/pip/SQL state. Network exfiltration and persistence are a different threat model, out of scope by design — not an unfinished corner. That's the boundary, drawn on purpose. See bench/.
uv run python -m blast_scope.eval                 # in-repo corpus

python bench/saber_eval.py --tasks /dataset/data/tasks.jsonl # SABER


Installation

The fastest path for any MCP client is zero-install via uvx (no clone, no venv):

uvx blast-scope        # runs the MCP server on stdio

Claude Code users — one line wires up both the MCP tools and the advisory hook:

/plugin marketplace add Atharva-Jayappa/blast-scope

/plugin install blast-scope

For development, or to pin a checkout:

git clone https://github.com/Atharva-Jayappa/blast-scope.git

cd blast-scope && uv sync --all-extras


Usage

As an MCP server

Add to your MCP client config (e.g. Claude Code settings.json):

{

"mcpServers": { "blast-scope": { "command": "uvx", "args": ["blast-scope"], "type": "stdio" } } }

Tools exposed:

ToolPurpose
assess_command(command, cwd?, project_root?)Score a (possibly chained) command. Returns score, severity, rationale, evidence, recoverability, affected nodes, and a per-segment chain breakdown.
index_project(project_root)Force a dependency-graph rebuild (auto-built on first use otherwise).
list_snapshots(project_root)List undo snapshots, newest first.
restore_snapshot(snapshot_id, project_root)Undo a risky command by restoring its snapshot.
As a hook (tiered advice + auto-snapshot)

Intercept Bash commands *before* they run — advisory, never blocking. Volume scales with stakes: silent on low/medium, advise on high, advise + snapshot on critical. The snapshot skips what's already recoverable (git-clean, regenerable) and warns rather than tars anything over a hard size cap, so the undo net stays fast and trustworthy.

The hooks also keep the dependency graph alive on their own — no MCP call needed: SessionStart cold-builds it in a detached background process, and every PreToolUse refreshes it incrementally before scoring (a ~20 ms stat sweep when nothing changed), so verdicts track the current tree even after a burst of agent edits. Add to .claude/settings.json:

{

"hooks": { "SessionStart": [ { "hooks": [{ "type": "command", "command": "python -m blast_scope.hook" }] } ], "PreToolUse": [ { "matcher": "Bash", "hooks": [{ "type": "command", "command": "python -m blast_scope.hook" }] } ] } }

Full details and the undo flow: docs/hook.md.


Example output

A filesystem command, scored against the dependency graph:

// assess_command("rm -rf ./config", project_root="/proj")

{ "score": 0.93, "severity": "critical", "recommendation": "block", "recoverability": "untracked", "rationale": "rm targets config. 8 direct importer(s), 14 total affected. not git-tracked. recursive deletion. CRITICAL risk.", "evidence": [ "8 importer(s), 14 affected node(s)", "high centrality (PageRank 0.91) — a hub other code routes through", "untracked — not in git history", "recursive — applies to every file underneath" ], "affected_nodes": [ /* ... */ ], "chain": [ /* per-segment breakdown */ ] }

A command class

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

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