shugo
Click on "Install 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., "@shugoblock all write operations to the production database"
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.
Documentation · Quickstart · Repo Structure · Policies · Roadmap
A guardrail should not merely tell an agent what it should do. The system should enforce what the agent is allowed to do.
Layers ③ and ⑤ ship complete in v0.1. The others ship at the minimum depth needed to make those two credible, then deepen along the roadmap.
⚙️ How it works
SHUGO presents itself to your agent as a standard MCP server and sits between the agent and the tools it calls. Every tools/call is evaluated against a human-readable policy file before it reaches an upstream server. No protocol changes. No agent code changes.
┌──────────────┐ MCP ┌───────────┐ MCP ┌──────────────────┐
│ MCP client │ ───────▶ │ SHUGO │ ───────▶ │ Upstream MCP │
│ (any agent) │ ◀─────── │ guard │ ◀─────── │ servers (n) │
└──────────────┘ └─────┬─────┘ └──────────────────┘
│
┌───────────┼───────────┐
▼ ▼ ▼
guardrails audit log approval
.yaml (JSONL) channelWorks with Claude Desktop, Claude Code, Cursor, VS Code, and any framework with an MCP adapter — LangGraph, CrewAI, the OpenAI Agents SDK, n8n.
Related MCP server: flux7-mesh
⚡ Quickstart
uvx shugo init # scaffold a policy from your existing MCP config
uvx shugo serve # start guardingPoint your MCP client at SHUGO instead of your servers:
{
"mcpServers": {
"shugo": {
"command": "uvx",
"args": ["shugo", "serve", "--config", "guardrails.yaml"]
}
}
}Your agent now sees only the tools policy allows, blocks on anything needing approval, and writes an audit record for every call it makes.
📜 Policy as code
The most important file in the project. Design target: a risk lead can read it unaided, and a reviewer can diff it in a pull request.
version: "0.1"
defaults:
decision: deny # deny by default; allowlist upward
on_error: deny # fail closed
rules:
- id: read-only-github
match:
server: github
tool: ["get_*", "list_*", "search_*"]
decision: allow
- id: no-force-push
description: Force push can destroy history irrecoverably
match:
server: github
tool: push
args: { force: true }
decision: deny
reason: Force push is prohibited for autonomous agents.
controls: [OWASP-LLM06, NIST-AI-RMF-MANAGE-2.2]
- id: ticket-write-needs-approval
match:
server: tickets
tool: ["update_ticket", "close_ticket"]
decision: escalate
approval:
channel: cli
timeout_seconds: 300
on_timeout: deny
controls: [EU-AI-ACT-ART-14]Deny by default. First match wins, top to bottom. Globs, not regex — readability is the constraint.
🧾 Evidence, not just logs
Enforcement without evidence is unauditable. Evidence without enforcement is theatre. SHUGO does both.
shugo evidence --framework owasp-llm --since 30d --out evidence/Reads the audit log and produces a control-by-control bundle: which rules were in force, how often each fired, approval latency, exceptions, coverage gaps, and a hash-chain integrity check.
Framework mappings ship as data files sourced from public standards only — NIST AI RMF, EU AI Act, OWASP Top 10 for LLM Applications, and ISO/IEC 42001 control identifiers.
Scope note. SHUGO produces evidence about the guardrail layer. It does not certify an organisation as compliant with any standard.
🧰 CLI
Command | Purpose |
| Run the guard proxy |
| Scaffold a starter policy from installed MCP servers |
| Lint and schema-check the policy file |
| Dry-run a call — see which rule fires and why |
| Inspect the log, verify the hash chain |
| Generate an evidence bundle |
| Kill switch — deny all calls immediately |
📦 Release notes — v0.1.0 (2026-07-30)
First release. Everything in the sections above is now real code you can install with uvx shugo.
What ships:
Guard proxy over stdio — SHUGO speaks MCP to both your client and any number of upstream servers, namespaces tools as
<server>__<tool>, and applies policy on everytools/call.Policy engine — deny-by-default, first-match-wins,
fnmatchglobs on server/tool, nested-equality on args. Decisions:allow,deny,escalate.Human approvals — file-drop queue at
~/.shugo/pending/resolved by eithershugo approve --watch(Rich TUI) or an opt-in local HTTP UI (shugo serve --approvals http).Hash-chained audit log — append-only JSONL, rolling SHA-256, NFC-canonical JSON, per-line
fsync.shugo audit verifydetects any post-hoc edit.Evidence bundles —
shugo evidence -f <owasp-llm|nist-ai-rmf|eu-ai-act|iso-42001>produces a control-mapped report, policy snapshot, filtered audit-log window, and SHA-256 manifest.shugo init— reads Claude Desktop / Code / Cursor / VS Code MCP configs, enumerates upstream tools, writes a starter policy, prints the exact JSON snippet to paste.Kill switch —
shugo haltdenies everything untilshugo unhalt.
Quality: 102 fast tests + 1 e2e test (real @modelcontextprotocol/server-filesystem via npx). CI on Python 3.11–3.13 × macOS / Ubuntu / Windows. All dependencies MIT / BSD-3 / Apache-2.0; no copyleft, no BSL.
Full changelog: CHANGELOG.md · GitHub release: v0.1.0
🗺️ Roadmap
Version | Theme | Status |
v0.1 | Guard proxy, policy engine, approvals, audit log, evidence packs | ✅ released 2026-07-30 |
v0.2 | Layer ① depth — OAuth 2.1 / OIDC identity, per-principal policy, delegation scoping | planned |
v0.3 | Layer ② depth — retrieval boundaries, context filters, memory access rules | planned |
v0.4 | Layer ④ depth — injection detection, output validation, rollback | planned |
v0.5 | Layer ⑤ depth — policy regression suites, adversarial harness | planned |
v1.0 | Frozen policy schema, semantic versioning guarantee, plugin API | planned |
🤝 Contributing
SHUGO is built in public, and the contribution ladder is deliberately low to the ground:
policy pack → scenario test → framework mapping → core
A new policy pack is a self-contained YAML file. You do not need to read the codebase to ship one. Start with CONTRIBUTING.md and the good first issues.
📄 License
Released under the MIT License. Use it, fork it, adapt it to your own agent architecture — that is the point.
守護 — shugo, to guard and protect.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
Alicense-qualityAmaintenanceOpen-source MCP proxy that enforces security policies, content scanning, and audit logging between AI agents and tool serversLast updated25AGPL 3.0- Alicense-qualityAmaintenanceGuardrail sidecar proxy between AI agents and their MCP/REST/CLI tools. Policy engine, human approval gates, time-limited grants, rate limiting, and OTEL tracing. One Go binary, one YAML config, fail-closed by default.Last updated1Apache 2.0
- Alicense-qualityCmaintenanceMCP server for AI agent security guardrails. Provides input validation, prompt injection detection, PII redaction, output filtering, policy enforcement, rate limiting, and comprehensive audit logging.Last updated501MIT
- Alicense-qualityBmaintenanceAn open-source MCP server that protects AI agents at runtime by evaluating every tool call against YAML policies and generating audit trails.Last updated1Apache 2.0
Related MCP Connectors
Runtime permission, approval, and audit layer for AI agent tool execution.
Security firewall for AI agents — scans MCP calls for injection, secrets, and risks.
Responsible-AI guardrails for agents: scoring with policy, injection & PII detection, DPDP.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/aritraghosh01/shugo'
If you have feedback or need assistance with the MCP directory API, please join our Discord server