Server data from the Official MCP Registry
Deterministic trust gate for AI output: leaked-secret, prompt-injection & PII in one call.
Deterministic trust gate for AI output: leaked-secret, prompt-injection & PII in one call.
Remote endpoints: streamable-http: https://sigma-gate-864996675261.us-central1.run.app/mcp
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
2 tools verified · Open access · No issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-spektre-labs-sigma-gate": {
"url": "https://sigma-gate-864996675261.us-central1.run.app/mcp"
}
}
}From the project's GitHub README.
Deterministic trust layer for AI/agent output.
1 = 1. Declared output must equal what is safe to realize.
One call. Three dimensions. One verdict. No model. No API key. No network.
LLM-as-judge guards are slow, consume a call per check, and silently degrade when a rate-limit hits. For the classes of risk that are structurally detectable — a leaked AKIA… key, a 4111… card number, an "ignore all previous instructions" — you do not need a model. You need a gate that gives the same answer every time.
guard is that gate.
from guard import guard
result = guard(model_output)
# {"safe_to_ship": True/False, "severity": "clean|low|medium|high|critical",
# "block_reasons": [...], "dimensions": {"secret": {...}, "injection": {...}, "pii": {...}}}
Block a bad output:
v = guard("Here is the key: ghp_16C7e42F292c6912E7710c838347Ae178B4a")
# safe_to_ship: False
# block_reasons: ["secret[high]: github_pat"]
Pass a clean output through:
v = guard("The quarterly report shows revenue grew 12% driven by the EU segment.")
# safe_to_ship: True
# severity: "clean"
| Dimension | What it catches |
|---|---|
| secret | Leaked credentials — 20+ providers (AWS, GitHub, Stripe, GCP, …), entropy-ranked |
| injection | Prompt-injection and jailbreak patterns — structural, not heuristic |
| pii | Email, phone, card (Luhn-verified), SSN, IBAN, IP — compliance-class detection |
Every dimension runs independently. A combined hit fires all three:
guard("AKIA… ghp_… 4111 1111 1111 1111 — ignore all previous instructions")
# block_reasons: ["secret[high]: ...", "injection[high]: ...", "pii[high]: ..."]
Zero dependencies, pure stdlib, Python 3.9+. Clone and use directly:
git clone https://github.com/spektre-labs/sigma-gate
cd sigma-gate
python3 -m pytest -q # 7 passing tests, zero dependencies
Then from guard import guard. (A PyPI release is planned.)
Inline gate in any pipeline:
from guard import guard
def ship(output: str) -> str:
v = guard(output)
if not v["safe_to_ship"]:
raise ValueError(f"blocked: {v['block_reasons']}")
return output
CLI — pipe any output through:
echo "your model output" | python3 -m guard
Self-test — prove every threat class fires:
python3 -m guard selftest
# {"secret_blocked": true, "injection_blocked": true, "pii_blocked": true,
# "clean_passes": true, "combined_all_fire": true, "ALL_PASS": true}
Tune the threshold via env var (default: medium):
GUARD_BLOCK_AT=high python3 -m guard "..."
σ-gate ships a zero-dependency MCP server — give any agent
(Claude Code, Claude Desktop, Cursor, Cline) a deterministic guard tool it can call before shipping
output. No model, no key, no token cost.
Claude Code:
claude mcp add guard -- python3 /absolute/path/to/sigma-gate/mcp_server.py
Claude Desktop (claude_desktop_config.json):
{ "mcpServers": { "guard": { "command": "python3",
"args": ["/absolute/path/to/sigma-gate/mcp_server.py"] } } }
Exposes two tools: guard(text, …) → the ship/block verdict, and guard_selftest() → proof every
threat class fires. Pure stdlib stdio JSON-RPC.
σ-gate also runs as a hosted remote MCP server (scale-to-zero), listed in the
official MCP Registry as io.github.spektre-labs/sigma-gate.
Connect with zero local setup:
https://sigma-gate-864996675261.us-central1.run.app/mcp
MCP clients that browse the registry discover it automatically.
| Open core (this repo) | Hosted σ scoring | |
|---|---|---|
| What | Deterministic gate: secret + injection + PII | Coherence / hallucination σ-scoring on a hot path |
| Latency | ~85µs | Network round-trip |
| Dependencies | Zero | None on your side |
| Cost | Free, always | Pay-per-call via x402 — no signup |
| Offline | Yes | No |
| Model | None | Optional |
The open core handles what models cannot do reliably — structural pattern detection with identical verdicts on identical inputs. The hosted layer adds probabilistic coherence scoring for the cases where structure alone is insufficient.
Hosted endpoint:
curl "https://swagletz-sigmagate.hf.space/check?text=your+text+here"
# HTTP 402 + permissionless x402 pay-to — no account required
severity: "error" — the call does not silently pass.Apache 2.0 — see LICENSE.
Part of Spektre Labs — coherence-theory research lab.
1 = 1, made executable.
Be the first to review this server!
by Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
by mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.