"Cloudflare Workers" matching MCP servers:

Delegate coding tasks to external AI coding agents in isolated git worktrees with independent verification, enabling any MCP client to orchestrate multi-agent workflows.

A Windows-native MCP gateway that lets any MCP-capable host orchestrate local coding-agent CLIs as external workers.

An MCP server that lets Claude spawn Cursor subagents as async, bounded workers, enabling non-blocking task delegation with asynchronous completion notifications.

MCP server that enables a coordinator AI agent to spawn, control, and supervise local coding agents with interactive gating for high-risk operations.

Enables non-coders to build software by describing ideas in natural language, orchestrating AI agents and CLI workers via chat without needing a terminal.

Enables orchestration of multiple Jules AI workers for tasks like code generation, bug fixing, and review using the Google Jules API. It features git integration, a shared memory system, and real-time activity monitoring for complex, multi-agent development workflows.

An MCP server for orchestrating a fleet of CLI coding agents in isolated git worktrees. It exposes tools for spawning workers, sending instructions, reviewing diffs, and merging changes, with full terminal visibility.

Enables agents to delegate coding tasks to self-hosted Hermes workers running in isolated container dev boxes, each with its own workspace, browser, and optional desktop, then monitor progress and collect screenshots, recordings, and PR evidence.

Enables AI supervisors to delegate coding tasks to sandboxed coding-agent workers on a Linux machine, with isolated git worktrees, daemon-run tests, and structured results.

An MCP server that uses Google Antigravity subagents as parallel workers for coding agents, enabling efficient task delegation with compact results.

Turns bounded coding tasks into reviewable Git diffs using isolated worktrees, configurable worker models, and test execution.

Enables Codex to orchestrate multi-agent coding by routing tasks to local Claude Code workers via CCSwitch, managing cost and model selection for planning, execution, and review.

An autonomous multi-agent orchestration layer that generates a bespoke software engineering organization for a given objective, runs parallel OpenCode workers in isolated git worktrees, and integrates reviewed code through a replan loop.

Offloads expensive code reading, editing, and checking tasks to DeepSeek through Claude Code, saving Codex main-thread tokens by 40-60%.

Enables a primary agent to dispatch, wait on, and supervise multiple parallel Codex CLI workers, each running in its own isolated Git worktree with conflict-checked path ownership. Persists every worker's state, goals, and event stream to disk so work can be tracked, resumed, reconnected, searched, and cancelled across restarts.

A Rust + Tauri 2 desktop application that turns local project folders into persistent MCP workspaces, enabling AI agents to read files, modify code, run commands and tests, check Git status, and maintain cross-conversation development context through historical checkpoints, with built-in WSS/FRP/Cloudflare tunnels and OAuth for connecting to ChatGPT.

Enables Codex to supervise one or more local Pi coding-agent workers through bounded task execution, state polling, steering, aborting, and listing. Tasks can run in a direct working directory or isolated Git worktree while Codex remains the planner and reviewer.

Enables STDIO-compatible AI assistants to queue coding work onto the official Google Antigravity CLI and track status, read reports or patches, query capabilities, or cancel running tasks without blocking. Supports trusted direct execution plus optional isolated or read-only modes depending on the platform.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.