Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Cordon for MCP. Security gateway with policy enforcement, audit log, and HITL approvals.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add cordon -- npx -y @getcordon/cli start프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"cordon": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 marras0914 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.3.0)은 6개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, FlyDocs
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
Spec-driven development workflow: your agent works issues, sessions and the status lifecycle.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Cordon for MCP
The Security Gateway for MCP Tool Calls
Website • Writeup
Quickstart • Why Cordon • How It Works • Configuration • Roadmap • Contributing
Every company wants to deploy AI agents. No company is willing to give an agent the keys to their database.
Cordon closes the trust gap.
From the maintainer of Agent Toolbelt — 25+ MCP tools, ~1,600 weekly npm downloads.
https://github.com/user-attachments/assets/153d978f-6303-443a-b49b-b4ec7ebf0452
The Model Context Protocol (MCP) has made it trivially easy to give AI agents access to powerful tools — databases, file systems, APIs, cloud infrastructure.
But MCP has no built-in security model. No audit logs. No approval workflows. No rate limits. Today, an AI agent is either off or full admin. There is nothing in between.
This is the single biggest blocker preventing AI agents from reaching production.
Cordon is the security gateway that sits between the LLM and your MCP servers.
It acts as a firewall, an auditor, and a remote control — giving you complete visibility and authority over what your AI agents can and cannot do.
┌─────────┐ ┌──────────┐ ┌──────────────┐
│ LLM / │ ──▶ │ Cordon │ ──▶ │ MCP Server │ │ Agent │ ◀── │ Gateway │ ◀── │ (database, │ └─────────┘ └──────────┘ │ fs, APIs) │ │ └──────────────┘ ├── Policy Engine ├── Audit Logger └── Approval Workflows
No infrastructure changes. No rewrites. One config file.
Step 1 — Initialize
Run this inside your project (where your claude_desktop_config.json exists):
npx @getcordon/cli init
This reads your existing Claude Desktop MCP config, generates cordon.config.ts, and patches Claude Desktop to route all tool calls through Cordon.
Step 2 — Start
npx @getcordon/cli start
Cordon starts, connects to your MCP servers, and begins intercepting tool calls. Restart Claude Desktop and every tool call now flows through the gateway.
If you prefer to configure manually, install globally and create a config:
npm install -g @getcordon/cli
cordon init
cordon init generates a cordon.config.ts:
import { defineConfig } from '@getcordon/policy';
export default defineConfig({ servers: [ { name: 'database', transport: 'stdio', command: 'npx', args: ['-y', '@my-org/db-mcp-server'], policy: 'read-only', // Block all write operations }, { name: 'github', transport: 'stdio', command: 'npx', args: ['-y', '@modelcontextprotocol/server-github'], policy: 'approve-writes', // Reads pass; writes require approval tools: { delete_branch: 'block', // Never, regardless of approval }, }, ],
audit: { enabled: true, output: 'stdout', // or 'file' },
approvals: { channel: 'terminal', timeoutMs: 60_000, // auto-deny after 60s; the request is retained // and replayable if approved late }, });
| Without Cordon | With Cordon |
|---|---|
| Agent has unrestricted tool access | Granular per-tool policies |
| No visibility into what agents did | Structured audit trail of every call |
| "Did the agent just drop a table?" | Real-time terminal approvals |
| Reads and writes treated the same | approve-writes lets reads through automatically |
| Compliance team says no to AI | Audit logs ready for export |
Define rules per tool, per server, or globally. Tool-level policies override server policies.
// Server-level default
policy: 'approve-writes',
// Per-tool overrides tools: { query: 'allow', // reads: pass through execute: 'approve', // writes: pause for human approval drop_table: 'block', // catastrophic: always reject list_tables: 'log-only', // audit but don't interrupt },
When a tool call requires approval, Cordon pauses the agent and prompts you directly in your terminal:
╔══════════════════════════════════════╗
║ ⚠ APPROVAL REQUIRED ║ ╚══════════════════════════════════════╝ Server : database Tool : execute_sql Args : { "query": "DELETE FROM sessions WHERE expires_at < NOW()" }
[A]pprove [D]eny
The agent waits. You decide.
Prefer Slack? Connect your workspace once with Add to Slack in the dashboard — no bot token to create or paste — then set approvals: { channel: 'slack' }. A flagged call posts an Approve / Deny card to your channel and pauses until a human clicks, and the approver's name is written onto the audit record. See the Slack approvals setup.
When nobody answers in time. The call is denied and the agent moves on, but the request isn't lost. Cordon retains the pending approval with its full context, and the dashboard lists it under What changed → Timed-out approvals. If someone approves the card after it expired, that late decision is recorded and the call can be re-run:
cordon replay <callId>
The replay executes the tool and logs the outcome to the same audit stream with reason replay of late-approved call. It recovers the tool call, not the agent's session — the agent that asked is long gone. Only a call approved *after* timing out is replayable, since replaying a call that already ran would double-execute it.
Every tool call is logged as structured JSON — the request, the policy decision, the response, and timing. Pipe to stdout or write to a file for your compliance team.
{"event":"tool_call_received","callId":"...","serverName":"database","toolName":"execute_sql","timestamp":1773434469641}
{"event":"approval_requested","callId":"...","serverName":"database","toolName":"execute_sql","timestamp":1773434469641} {"event":"tool_call_approved","callId":"...","serverName":"database","toolName":"execute_sql","timestamp":1773434471203} {"event":"tool_call_completed","callId":"...","durationMs":34,"isError":false,"timestamp":1773434471237}
One policy setting to block all write operations across a server. Zero guesswork about what counts as a write — Cordon detects it from the tool name.
policy: 'read-only' // any tool starting with write/create/update/delete/drop/execute/... is blocked
For tools the model should never even see — not just rejected on call, but filtered from the tools/list response entirely. Closes a prompt-injection surface: if the model never knows a tool exists, it can't be tricked into calling it.
{
name: 'database', policy: 'approve-writes', tools: { drop_table: 'block', // call attempts are rejected internal_admin: 'hidden', // not advertised to the client at all }, }
For database MCP servers where a single tool takes arbitrary SQL (Postgres, SQLite, BigQuery, etc.), tool-name heuristics aren't precise enough — the name query doesn't tell you whether the agent's about to SELECT or DROP TABLE. Cordon ships two policies that parse the SQL itself and decide based on the statement type.
tools: {
// Allow SELECTs (including CTEs that wrap a SELECT). Block everything else. query: 'sql-read-only',
// Reads pass; writes (INSERT/UPDATE/DELETE/DROP/ALTER/...) pause for human approval. execute: 'sql-approve-writes',
// When the tool takes SQL in a different arg name: run: { action: 'sql-read-only', sqlArg: 'statement' }, }
Both policies use the PostgreSQL dialect by default (others coming later) and are fail-closed: unparseable SQL is blocked rather than allowed. Prompt-injection patterns like SELECT 1; DROP TABLE users; and block-comment-wrapped keywords are correctly classified as writes by the AST parser.
Declare the exact tool surface your upstream server is expected to advertise. When the upstream adds a new tool in a future release, Cordon blocks it automatically until you explicitly promote it.
{
name: 'postgres', command: 'npx', args: ['-y', '@modelcontextprotocol/server-postgres', process.env.POSTGRES_URL!], policy: 'read-only', knownTools: ['query', 'list_tables', 'describe_table'], // your approved surface onUnknownTool: 'block', // default when knownTools is set }
If the next Postgres MCP release adds truncate_table, Cordon blocks it with a stderr warning — no policy update needed. Leave knownTools undefined for backwards-compatible open-world behavior.
Per-tool policies catch dangerous *individual* calls. Call-graph rules catch dangerous *sequences* — combinations of allowed tools used in unintended order. The classic example: an agent reads sensitive data with read_data (allowed) and then writes it to disk with write_file (also allowed). Each call is fine in isolation. The *sequence* is the problem.
Cordon's call-graph rules apply on top of your per-tool policy and are additive — they can only raise severity, never lower it.
export default defineConfig({
// Optional identity for this Cordon process. Surfaced in audit logs. agentId: 'et-acquisition-agent',
servers: [{ name: 'demo-db', transport: 'stdio', command: 'npx', args: ['-y', '@my-org/db-mcp'], policy: 'approve-writes', }],
callGraph: [ // Reading data and then writing to disk is the exfil shape — block it, // even though write_file would only require approval on its own. { from: 'read_data', to: 'write_file', action: 'block', reason: 'No file writes after database reads.' },
// Glob matching: any send_* tool right after a database read needs human approval. { from: 'read_data', to: 'send_*', action: 'approve' },
// * matches anything: after a privileged read, every next call must be approved. { from: 'sensitive_read', to: '*', action: 'approve' }, ], });
How rules combine with per-tool policies (additive severity):
Severity ordering: allow < approve < block.
A call-graph rule only takes effect if its action is *higher* severity than the per-tool decision. That means:
action: 'block' overrides an allow per-tool policy ✓action: 'approve' overrides allow, but doesn't lower a block ✓action: 'allow' never weakens a stricter base — it's a no-opOne-way ratchet — tighten only, never loosen.
**Audit log gains chain co
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요