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The rustunnel-mcp binary implements the Model Context Protocol (MCP) over stdio, letting AI agents (Claude, GPT-4o, Gemini, custom agents) manage tunnels without any manual setup.

How it works

MCP is a standard for connecting AI agents to external tools. The agent sends JSON-RPC calls to the MCP server; the server translates them into REST API calls and CLI commands.
AI Agent ──── MCP (stdio) ────▶ rustunnel-mcp
                                    │               │
                              spawns rustunnel   calls /api/*
                              CLI subprocess     (REST API)
                                    │               │
                                    └───────────────▼
                                          rustunnel-server
Tunnels are established via a persistent WebSocket connection (the control plane on port 4040), not via a REST call. The MCP server handles this by spawning the rustunnel CLI as a subprocess when create_tunnel is called.

Installation

Build from source (included in the workspace):
make release-mcp
# Produces: target/release/rustunnel-mcp

# Install to PATH
sudo install -m755 target/release/rustunnel-mcp /usr/local/bin/rustunnel-mcp
Or build without the dashboard UI step:
cargo build --release -p rustunnel-mcp

Configuration

rustunnel-mcp takes two flags:
FlagDefaultDescription
--serverlocalhost:4040Control-plane address forwarded to the rustunnel CLI
--apihttp://localhost:4041Dashboard REST API base URL for tunnel queries
--insecurefalseSkip TLS certificate verification (local dev / self-signed certs)

Connecting an AI agent

Using the hosted server (edge.rustunnel.com)

The fastest way to get started. You need an auth token — see Getting an auth token in the README. Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
  "mcpServers": {
    "rustunnel": {
      "command": "rustunnel-mcp",
      "args": [
        "--server", "edge.rustunnel.com:4040",
        "--api",    "https://edge.rustunnel.com:8443"
      ],
      "env": {
        "RUSTUNNEL_TOKEN": "<your-token>"
      }
    }
  }
}
Claude Code — add to your project’s .mcp.json:
{
  "mcpServers": {
    "rustunnel": {
      "command": "rustunnel-mcp",
      "args": [
        "--server", "edge.rustunnel.com:4040",
        "--api",    "https://edge.rustunnel.com:8443"
      ]
    }
  }
}
Once connected, you can ask the agent:
“Expose my local server on port 3000 using my rustunnel token <token>.” “Open an HTTP tunnel to port 8080 with subdomain myapp.” “List all my active tunnels.”
The agent will call create_tunnel and return a public URL like https://abc123.edge.rustunnel.com.

Self-hosted server

Replace the server address with your own instance:
{
  "mcpServers": {
    "rustunnel": {
      "command": "rustunnel-mcp",
      "args": [
        "--server", "your-server.com:4040",
        "--api",    "https://your-server.com:8443"
      ]
    }
  }
}

Local development (self-signed cert)

{
  "mcpServers": {
    "rustunnel": {
      "command": "rustunnel-mcp",
      "args": [
        "--server",   "localhost:4040",
        "--api",      "http://localhost:4041",
        "--insecure"
      ]
    }
  }
}

Cursor / VS Code / any MCP client

Most MCP clients use the same JSON format. Consult your client’s documentation for the exact location of the config file.

Custom / programmatic agents

Spawn rustunnel-mcp as a subprocess and communicate via stdin/stdout using newline-delimited JSON-RPC 2.0:
import subprocess, json

proc = subprocess.Popen(
    ["rustunnel-mcp", "--server", "localhost:4040"],
    stdin=subprocess.PIPE,
    stdout=subprocess.PIPE,
)

def call(method, params=None, id=1):
    msg = {"jsonrpc": "2.0", "id": id, "method": method}
    if params:
        msg["params"] = params
    proc.stdin.write(json.dumps(msg).encode() + b"\n")
    proc.stdin.flush()
    return json.loads(proc.stdout.readline())

Available tools

create_tunnel

Open a tunnel to a locally running service and get a public URL.
ParameterTypeRequiredDescription
tokenstringyesAPI token for authentication
local_portintegeryesLocal port the service is listening on
protocol"http" | "tcp"yesTunnel type
subdomainstringnoCustom subdomain for HTTP tunnels
Returns:
{
  "public_url": "https://abc123.edge.rustunnel.com",
  "tunnel_id":  "a1b2c3d4-...",
  "protocol":   "http"
}
The MCP server spawns rustunnel as a background subprocess and polls the API until the tunnel appears (up to 15 seconds). The tunnel stays open until close_tunnel is called or the MCP server exits. Example agent prompt:
“Expose my local server on port 3000 using token abc123.”

list_tunnels

List all currently active tunnels.
ParameterTypeRequiredDescription
tokenstringyesAPI token for authentication
Returns: JSON array of tunnel objects from GET /api/tunnels.

close_tunnel

Force-close a tunnel. The public URL stops working immediately.
ParameterTypeRequiredDescription
tokenstringyesAPI token
tunnel_idstringyesUUID returned by create_tunnel or list_tunnels

get_connection_info

Returns the CLI command string without spawning anything. Use this when the MCP server cannot launch subprocesses (cloud sandboxes, containers) or when you want to run the CLI yourself.
ParameterTypeRequiredDescription
tokenstringyesAPI token
local_portintegeryesLocal port to expose
protocol"http" | "tcp"yesTunnel type
Returns:
{
  "cli_command":  "rustunnel http 3000 --server edge.rustunnel.com:4040 --token abc123",
  "server":       "edge.rustunnel.com:4040",
  "install_url":  "https://github.com/joaoh82/rustunnel/releases/latest"
}

get_tunnel_history

Retrieve the history of past tunnels.
ParameterTypeRequiredDescription
tokenstringyesAPI token
protocol"http" | "tcp"noFilter by protocol
limitintegernoMax entries to return (default: 25)

Agent workflow examples

Local agent exposing a dev server

1. Agent has a pre-existing API token (obtained from the dashboard or via
   the CLI: rustunnel token create --name agent-session)

2. Agent calls create_tunnel(token="...", local_port=3000, protocol="http")
   → MCP server spawns: rustunnel http 3000 --server ... --token ...
   → Returns: { public_url: "https://xyz.edge.rustunnel.com", tunnel_id: "..." }

3. Agent returns the public URL to the user.

4. Later: Agent calls close_tunnel(token="...", tunnel_id="...")
   → MCP server calls DELETE /api/tunnels/:id and kills the subprocess

Cloud agent (no subprocess access)

1. Agent calls get_connection_info(token="...", local_port=8000, protocol="http")
   → Returns: { cli_command: "rustunnel http 8000 --server ... --token ..." }

2. Agent outputs the command. User runs it in their local environment.

3. Agent calls list_tunnels(token="...") to confirm the tunnel is active
   and retrieve the public URL.

Token management

Hosted service

Sign up at rustunnel.com, then create an API key from the dashboard under Settings → API Keys.

Self-hosted server

Create tokens via the dashboard UI, the CLI, or the REST API:
# CLI
rustunnel token create --name agent-session

# REST API
curl -X POST https://edge.rustunnel.com:8443/api/tokens \
  -H "Authorization: Bearer <admin-token>" \
  -H "Content-Type: application/json" \
  -d '{"label": "agent-session"}'
Store the raw token value securely — it is shown only once at creation time.

OpenAPI spec

The server exposes a machine-readable API description at:
GET /api/openapi.json
No authentication required. Useful for agent discovery and client generation.
curl http://localhost:4041/api/openapi.json | jq .info

Security notes

  • The rustunnel binary must be installed and in PATH on the machine running rustunnel-mcp for create_tunnel to work.
  • Tokens passed to tools are sent to the rustunnel server over HTTPS (or HTTP in local dev). Use HTTPS in production.
  • Child processes spawned by create_tunnel are killed when the MCP server exits (stdin closes). They are not persisted across MCP server restarts.
  • Use --insecure only in local development with self-signed certificates.