Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@sigma-gate Check this output for secrets and PII: 'The meeting is at 3pm.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Deterministic trust layer for AI/agent output. One call → one verdict. No model, no API key, no network.
1 = 1. Declared output must equal what is safe to realize.The other four Spektre repos are routing protocols — they move value, capability, promises, and identity
across networks. σ-gate is not a routing protocol, and does not pretend to be one. It is the suite's
deterministic trust verdict: the gate that sits at the edge of any pipeline and answers, in ~85µs and
identically every time, is this output safe to ship? LLM-as-judge guards are slow, burn a model call per
check, and silently degrade under rate-limits. But the highest-frequency risks — a leaked AKIA… key, a
Luhn-valid card number, an "ignore all previous instructions" — are structurally detectable: you don't
need a model, you need a gate that gives the same answer every time. That determinism is the
primitive. σ = declared − realized: a block is always nameable (secret[high]: github_pat), never an
opaque float, so what the gate declares unsafe is exactly what it refuses to realize. guard is that gate.
Related MCP server: agent-safety-mcp
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+.
git clone https://github.com/spektre-labs/sigma-gate && cd sigma-gate
pip install -e . # installs the `guard` + `guard-mcp` console scripts
python3 -m pytest -q # 7 passed, zero dependenciesNo install is even required to import — from guard import guard works from the cloned directory.
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 outputCLI — pipe any output through:
echo "your model output" | python3 -m guardSelf-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.pyClaude 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/mcpMCP 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 requiredDeterministic. Same input → same verdict. No variance, no model drift.
Composable. Each dimension is independent and pluggable. Wire in a hallucination scorer or extend with custom patterns; the gate architecture is additive.
Honest. Severity and block-reasons are explicit strings, not opaque floats. A block is always nameable.
Fails safe. If a detector throws, that dimension returns severity: "error" — the call does not silently pass.
Zero dependencies. Runs anywhere Python 3.9+ runs. No pip install required to import.
REAL — shipped, deterministic, deployed. CI green, 7/7 tests passing, zero dependencies; live as a local MCP tool and as a hosted scale-to-zero MCP server listed in the official registry.
σ-gate is the deterministic trust verdict of a five-part estate. The other four are routing protocols; this one is the gate they ship through:
vrp — value routing (least-friction multi-hop settlement)
crp — capability routing (route a task to the best AI substrate)
vtc — verifiable transaction chain (signed value promises anyone verifies trustlessly)
sid — sovereign identity (prove one claim, reveal nothing else)
sigma-gate — deterministic trust verdict (this repo)
Apache-2.0 — see LICENSE.
Part of Spektre Labs — coherence-theory research lab.
σ = declared − realized · 1 = 1, made executable.
ONE deterministic pre-ship trust gate for AI/agent output. Runs leaked-secret detection (20+ providers), prompt-injection / jailbreak detection, and PII / compliance detection together -> returns safe_to_ship (bool) + block_reasons. No model, no API key, no token cost; same input gives the same verdict every time. Use before sending model output to a user, committing generated code, or forwarding untrusted text into another prompt.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | the text/output to gate before shipping | |
| context | No | optional ground-truth context (reserved) | |
| block_at | No | severity at which to mark unsafe (default medium) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the transparency burden. It discloses key behavioral aspects: determinism, no model/API key/token cost, the combined detection run, and the return value (safe_to_ship + block_reasons). It doesn't cover edge-case error behavior, but for a pure detection gate this is sufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured paragraph that front-loads the core purpose, then explains the detection types, determinism, and use cases. Every sentence adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no output schema, and no annotations, the description provides complete context: what it does, what checks it runs, how it behaves (deterministic, no cost), what it returns, and when to use it. It is sufficient for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional parameter information beyond what the schema already provides (e.g., it does not explain block_at or context in its own words). Thus it does not elevate the score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a deterministic pre-ship trust gate for AI/agent output, listing specific detection types (leaked secrets, prompt injection, PII/compliance). It distinguishes itself from the sibling guard_selftest by being the actual gate, not a self-test utility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: before sending model output to a user, committing generated code, or forwarding untrusted text into another prompt. It provides clear usage context but does not mention exclusions or alternatives, so it falls short of the top score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prove the gate works: runs a known secret, injection, PII, combined, and clean input and returns which classes fired. No arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It does specify that it runs known inputs and returns firing classes, but it omits any mention of side effects, safety, or what the output classes mean. The description is transparent about the action but lacks full behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the purpose and containing only essential details. It is efficient and well-structured without any filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter self-test, the description covers what it does and the return concept. However, since there is no output schema, it could be more complete by detailing the expected classes or output structure, but this is a minor gap given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the description explicitly states 'No arguments.' This fully addresses parameter semantics, matching the baseline for 0-param tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Prove') and enumerates the exact set of inputs it runs (secret, injection, PII, combined, clean) and that it returns which classes fired. This strongly differentiates it from the sibling tool 'guard' by indicating it's a self-test.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus the alternative 'guard', nor any exclusions or prerequisites. The name 'selftest' implies testing, but the description does not articulate a clear decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0guardguard_selftestScored across 2 tools
The two tools serve clearly different functions: guard performs the actual security detection, while guard_selftest verifies the gate's functionality. There is no ambiguity between running a check and testing the checker.
Both tools share the 'guard' prefix, making the relationship obvious. The names are short, readable, and follow a consistent pattern (guard and guard_selftest).
With only two tools, the server feels minimal but appropriately scoped for a single deterministic gate plus a self-test. The count is borderline on the low end but matches the focused utility.
The domain is fully covered: guard runs all detection types in one call and returns block reasons, while guard_selftest validates all detection classes. There are no missing operations for the stated purpose.
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