Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Cordon for MCP. Security gateway with policy enforcement, audit log, and HITL approvals.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add cordon -- npx -y @getcordon/cli startIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"cordon": {
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"cordon": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@getcordon/cli",
"start"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto marras0914 bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.3.0) kam vor 6 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Reelsaw
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Drive a self-hosted Reelsaw: turn long videos into vertical clips, edit, re-render and export them.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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
Nur der Anfang wird gezeigt; den Rest findest du im Repo