Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Cordon for MCP. Security gateway with policy enforcement, audit log, and HITL approvals.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}在终端中运行这条命令
claude mcp add cordon -- npx -y @getcordon/cli start粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"cordon": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 marras0914 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.3.0)于6个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, SuperCollider
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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.
Make sound with SuperCollider: synths, drums, chords, melodies and soundscapes over OSC.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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
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