Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Cordon for MCP. Security gateway with policy enforcement, audit log, and HITL approvals.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}ターミナルでこれを実行します
claude mcp add cordon -- npx -y @getcordon/cli startプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"cordon": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント marras0914 で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.3.0)がレジストリに届いたのは6 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, Tools MCP
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日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.
Web production tools for AI agents: contrast, JP text, WebP, JSON-LD, slugs, dates and 40+ more
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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
冒頭だけを表示しています。続きはリポジトリで読めます