openclaw-bridge-remote
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@openclaw-bridge-remotenavigate to example.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OpenClaw MCP Bridge
A high-performance bridge that exposes OpenClaw capabilities as a Model Context Protocol (MCP) server.
This bridge allows remote AI agents (like OpenCode, Cursor, or Windsurf running on remote servers) to securely delegate local tasks—such as browser automation and shell execution—to an OpenClaw Gateway running on your local machine (e.g., MacBook) via Tailscale or any network connection.
Why this exists
When working on powerful remote servers (GPU clusters, cloud instances, etc.), AI agents are "blind" to your local environment. This bridge provides the "eyes and hands" by connecting the remote agent to your local OpenClaw instance.
Local Browser Control: Navigate, screenshot, click, and scrape pages on your local machine.
Local Shell Access: Execute commands on your local OS (say, open, pbcopy, hardware control).
Secure Authentication: API key via HTTP headers + OpenClaw Ed25519 challenge-response signing.
Hybrid Routing: Intelligently routes commands between the OpenClaw Gateway and connected Nodes.
Related MCP server: WinRemote MCP
🎯 Use Cases
1. Remote Development with Local Eyes
Run heavy AI models or IDE backends on a powerful cloud server while letting the AI see and control your local browser for previewing changes.
2. "Job Done" Physical Notification (HomePod)
Combine this bridge with homepod-announcer-skill to let your remote AI agent speak to you when a long task completes.
Remote Agent: "Training finished. Sending notification to local HomePod."
Local Bridge: Receives request -> Forwards to local HomePod Skill.
HomePod: "Sir, your model training is complete with 98% accuracy."
Architecture
graph LR
subgraph Remote_Server
A[AI Agent / OpenCode] -- "SSE (MCP)" --> B[openclaw-bridge-remote]
end
subgraph Local_Machine
B -- "Tailscale/Network" --> C[OpenClaw Gateway]
C -- "WebSocket" --> D[Local Browser]
C -- "WebSocket" --> E[OpenClaw Node]
E -- "Exec" --> F[Local OS Shell]
endQuick Start
1. Install (on your local machine, e.g. MacBook)
curl -fsSL https://raw.githubusercontent.com/lucas-jo/openclaw-bridge-remote/main/install.sh | bashThe installer will:
Clone the repo to
~/openclaw-bridge-remoteInstall dependencies via Bun
Auto-detect your
OPENCLAW_GATEWAY_TOKENGenerate a random
BRIDGE_API_KEYCreate a
.envfile with all settings
2. Verify the bridge is running
# From your remote machine (replace with your local machine's IP)
curl http://<your-local-ip>:3100/health
# Expected: {"status":"ok","gateway":true,"version":"0.1.0"}3. Get the values you need for MCP config
You need two pieces of information from the .env file on your local machine:
# On your local machine (where the bridge is running)
cat ~/openclaw-bridge-remote/.envNote down:
BRIDGE_API_KEY— The secret key for authenticating MCP clientsYour local machine's IP — Use
tailscale ip -4(Tailscale) or your LAN IP
4. Configure your AI agent (on the remote machine)
Important: Use
headerswithx-api-keyfor authentication, NOT query parameters in the URL. The MCP SDK's SSE transport drops query parameters from the URL when making POST requests, which causes authentication failures.
OpenCode (~/.config/opencode/config.json):
{
"mcp": {
"openclaw-remote": {
"type": "remote",
"url": "http://<your-local-ip>:3100/sse",
"headers": {
"x-api-key": "<your-BRIDGE_API_KEY>"
},
"enabled": true
}
}
}Cursor / Windsurf / Claude Code (MCP config):
{
"mcpServers": {
"openclaw-remote": {
"type": "remote",
"url": "http://<your-local-ip>:3100/sse",
"headers": {
"x-api-key": "<your-BRIDGE_API_KEY>"
}
}
}
}Local agents (stdio mode) — For agents running on the same machine as the bridge:
{
"mcpServers": {
"openclaw-local": {
"command": "bun",
"args": ["run", "start", "--transport=stdio"],
"cwd": "~/openclaw-bridge-remote"
}
}
}5. Test the connection
After configuring your agent, try calling any tool (e.g. openclaw_gateway_call with method: "node.list") to verify the bridge is working end-to-end.
Configuration Reference
The .env file controls the bridge behavior:
Variable | Description | Default | How to find |
| Auth token for connecting to OpenClaw Gateway | Required | Run |
| Secret key for authenticating remote MCP clients | (Auto-generated) | Check |
| OpenClaw Gateway host |
| Usually localhost |
| OpenClaw Gateway port |
| Run |
| Port for the MCP SSE server |
| Any available port |
Running the Bridge
SSE Mode (for remote agents)
bun run start # Default: SSE mode
bun run start --transport=sse # ExplicitStdio Mode (for local agents)
bun run start --transport=stdioAvailable MCP Tools
Tool | Description |
| Unified browser control (navigate, click, type, tabs, open, evaluate) |
| Capture browser screenshot (PNG/JPEG) |
| Get accessibility tree for AI reasoning (with smart pruning) |
| Execute shell commands on the local machine |
| Direct RPC access to OpenClaw Gateway (e.g. |
Required: Browser Relay Configuration
If you run both the Gateway and Node Host (needed for openclaw_system_run), you must add this to ~/.openclaw/openclaw.json to prevent EADDRINUSE port conflicts on the browser relay:
{
"gateway": {
"nodes": {
"browser": {
"mode": "off"
}
}
}
}This tells the Gateway to handle browser requests directly instead of proxying them to the Node Host (which would try to bind the same relay port). See SETUP.md for details.
Documentation
SETUP.md: Detailed architecture, protocol details, browser relay config, and troubleshooting.
RECIPES.md: Connectivity options (Tailscale, SSH Tunneling, Cloudflare Tunnel).
Development
bun run dev # Watch mode (auto-restart on changes)
bun run lint # Check code style
bun run format # Auto-fix code style
bun run typecheck # Check for type errorsReleasing
Update version in
package.json.Push a tag:
git tag v0.2.1
git push origin v0.2.1The Release Workflow automatically runs checks and creates a GitHub Release.
License
MIT © Lucas Jo
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