Work-queue kernel for AI coding agents: dependencies, worktree isolation, quality gates, recovery
Run one of the commands below, then add the client config underneath.
Pythonuvx ddflow-mcpDockerdocker run -i --rm docker.io/delian/ddflow-mcp:0.1.13Dockerdocker run -i --rm ghcr.io/delian/ddflow-mcp:0.1.13
Paste into Claude Desktop, Cursor (mcp.json), VS Code or any MCP client, then restart the client.
mcpServers{
"mcpServers": {
"ddflow-mcp": {
"command": "uvx",
"args": [
"ddflow-mcp"
]
}
}
}
ddflow is listed in the Developer Tools category of the MCPNav directory. It is distributed as Python, Docker and can be loaded by any client that speaks the Model Context Protocol.
Typical uses include giving your assistant scoped access to the corresponding service so it can answer questions and take actions with real data instead of guessing. Always review what a server can access before you enable it — see our MCP security guide.
ddflow is an MCP server by delian. Work-queue kernel for AI coding agents: dependencies, worktree isolation, quality gates, recovery
Install it with: uvx ddflow-mcp. Then add the JSON config to your client's MCP settings and restart the client.
The MCP server itself is free to install. The source is public on https://github.com/delian/ddflow-mcp. Any third-party API it calls (such as a search or maps API) may require its own key and billing.
Any MCP-compatible client can use it, including Claude Desktop, Cursor, VS Code, Windsurf and custom agents.