Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Contextual blast-radius scoring for shell commands an AI agent is about to run
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"blast-scope": {
"command": "uvx",
"args": [
"blast-scope"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add blast-scope -- uvx blast-scopeIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"blast-scope": {
"command": "uvx",
"args": [
"blast-scope"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"blast-scope": {
"type": "stdio",
"command": "uvx",
"args": [
"blast-scope"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto Atharva-Jayappa bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.6.1) kam vor 3 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Toggl 2.0
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Toggl 2.0 MCP server for tasks, projects, time entries, and workspace operations.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.
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.
| Class | Destructive ops it scores | Safe (read-only) probe | Reversibility signal |
|---|---|---|---|
| Filesystem | rm -rf, mv, > truncate | dependency graph + git status | git-tracked? regenerable? secret? precious? |
| Git | reset --hard, push --force, branch -D, clean -fdx | status · reflog · rev-list · rev-parse @{u} | reflog window · remote ahead · protected branch |
| Docker | volume rm, system prune -a, rm -f | volume inspect · ps -a · volume ls | volume → none · container → recreatable from image |
| pip / uv | pip uninstall, uv pip uninstall | read lockfile / manifest (no subprocess) | lockfile present → fully regenerable |
| SQL | DROP, TRUNCATE, DELETE without WHERE | SQLite: SELECT count(*) mode=ro; transaction check | inside 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.
Calibrated multi-class guardrail with command resolution and a precise dependency graph.
| Capability | Module |
|---|---|
| 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 $(...) substitution | resolution.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 snapshot | classes/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 indexing | graph_resolver.py, centrality.py |
| Two-axis, evidence-based filesystem scoring | risk_scorer.py |
| Command-class probes — git / docker / pip·uv / SQL, behind one protocol | classes/ |
| Out-of-graph path analyzers (infra / config-by-path) + git base | consequences.py, vcs.py, infra.py, config_refs.py |
| PreToolUse hook + tarball snapshot/undo | hook.py, snapshot.py |
| Eval harness + labeled corpus + calibration | eval.py, tests/fixtures/eval_corpus.jsonl |
Calibration. Two harnesses, both run-it-yourself:
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.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
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
Add to your MCP client config (e.g. Claude Code settings.json):
{
"mcpServers": { "blast-scope": { "command": "uvx", "args": ["blast-scope"], "type": "stdio" } } }
Tools exposed:
| Tool | Purpose |
|---|---|
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. |
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.
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
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