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MCP Protocol

The Model Context Protocol (MCP) lets AI agents use external tools — file systems, databases, browsers, and custom services. GWC supports MCP natively in both Studio and Cloud.

How it works

  1. Add an MCP server — go to Settings → Integrations → MCP Servers in your dashboard and register your server’s URL (or POST /api/mcp via the API).
  2. Agent discovers tools — when your agent runs, GWC exposes the registered MCP tools automatically.
  3. Agent calls tools — the agent decides which tool to use, sends a request, and gets structured results back.
  4. Where tools run — Studio runs MCP tools locally on your machine. Cloud-registered MCP servers are invoked by GWC’s API on your behalf — tool inputs and outputs pass through GWC infrastructure.

Connect your own MCP servers

Register public MCP endpoints from your dashboard (Settings → Integrations → MCP Servers) or via POST /api/mcp, and add Studio-local servers with the gwc.mcpServers setting.

Taichu MCP →

Use GWC's image/video/chat/agent-orchestration stack from Claude Code, Codex, or Cursor. One token, five tools.

Custom MCP Server

Connect any MCP-compatible tool server.

Custom MCP server

Add your own MCP-compatible tool server in GWC Studio settings:

// Example: custom MCP server config in GWC Studio
{
  "mcpServers": {
    "my-tool": {
      "command": "node",
      "args": ["./my-mcp-server.js"],
      "env": {
        "API_KEY": "${env:MY_API_KEY}"
      }
    }
  }
}

Creating tasks via SDK

Create tasks programmatically — when your agent runs, it discovers the MCP tools registered to your workspace automatically:

// Create a task via the SDK
const gwc = new GWC({ apiKey: 'your-key' });

await gwc.task.create({
  name: 'Analyze database',
  agent: 'data-analyst',
});
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MCP Server Hosting — Coming Soon

GWC will soon let you deploy and host MCP servers directly from your dashboard, removing the need to run them locally. Your tools stay hosted and online even when your machine is off. Stay tuned for updates.