@cinch-codes/mcp

@cinch-codes/mcp

MCP server for Cinch. Gives your AI assistant a real sandbox to run code in.

Without it, an assistant writes code and you run it yourself. With it, the assistant runs the code and reads the actual output — inside a gVisor-isolated container on Cinch's infrastructure, with no access to your machine, filesystem, or local network.

Pay per execution. No subscription floor.

Setup

Get an API key at cinch.codes, then add the server to your MCP client.

Claude Desktop — claude_desktop_config.json:

{
  "mcpServers": {
    "cinch": {
      "command": "npx",
      "args": ["-y", "@cinch-codes/mcp"],
      "env": {
        "CINCH_API_KEY": "cinch_live_..."
      }
    }
  }
}

Claude Code — from your project directory:

claude mcp add cinch -e CINCH_API_KEY=cinch_live_... -- npx -y @cinch-codes/mcp

Cursor — .cursor/mcp.json, same shape as the Claude Desktop config above.

Restart the client. That's it — no install step, npx fetches it on first run.

What it exposes

execute_code

Runs a self-contained Python or JavaScript program and returns stdout, stderr, exit code, and duration.

Parameter	Type	Default	Description
code	string	—	The complete program to run. Must print to stdout to return anything.
language	python | javascript	python	Runtime to execute in.

Each call gets a clean sandbox. State does not persist between calls, so every snippet needs to stand on its own.

The sandbox environment

Deliberately minimal. Worth knowing before you wonder why an import failed:

Runtimes	Python 3.12, Node 20
Packages	Standard library only. No pip or npm packages are installed, and none can be installed at runtime.
Network	None. HTTP, DNS, and package installs all fail.
Filesystem	Root is read-only. /tmp is writable (64 MB) and destroyed when the run ends.
Memory	256 MB
CPU	0.5 cores
Time limit	10 seconds
Isolation	gVisor (runsc), all capabilities dropped, no-new-privileges, non-root user, 64-process cap

The tool description tells the model all of this up front, so it writes stdlib-only code instead of reaching for numpy and failing on the first call.

Configuration

Variable	Required	Default	Description
CINCH_API_KEY	yes	—	Your Cinch API key.
CINCH_TIMEOUT_MS	no	20000	Client-side timeout in ms. The API caps execution at 10s regardless.
CINCH_BASE_URL	no	https://api.cinch.codes	Override the API endpoint.

Why sandboxed execution

Code written by a model is untrusted code — nothing reviewed it before it ran. Executing it directly on your machine means handing it your filesystem, your network, and your credentials. Cinch runs it somewhere else entirely, in a disposable container with kernel-level isolation, and sends back only the output.

Notes

• Requires Node 18 or newer.
• Output is capped at 20,000 characters per stream to protect your context window; anything beyond that is truncated with a marker.
• Runs that time out or exit non-zero are returned as tool errors, so the assistant knows the code failed and can correct it.
• Out-of-credit responses are surfaced clearly and instruct the assistant not to retry, so a drained balance does not turn into a retry loop.

Links

• cinch.codes
• JS/TS SDK: @cinch-codes/pangolin
• Python SDK: pangolin-sdk

MIT