mcp-swarm-router

mcp-swarm-router

This MCP server enables LLM hosts to plan large tasks and delegate subtasks to specialized local CLI agents, such as Codex and Claude Code, using tools like delegate_task and get_agent_roster.

Category
访问服务器

README

MCP-Swarm-Router

An MCP server that lets an LLM host (e.g. Claude Code) plan a large task, then delegate subtasks to local CLI agents based on their specialization — a "swarm router" for tools already installed on your machine.

Roster

id specialization CLI assumed non-interactive invocation
codex Image generation & heavy tools OpenAI Codex CLI codex exec --sandbox workspace-write "<prompt>"
agy UI/UX design & creative Google Antigravity CLI (agy, Gemini-based) — unverified, swap via env if wrong agy -p "<prompt>" --dangerously-skip-permissions
claude-code Hard coding & logic Claude Code claude -p "<prompt>" --output-format text --permission-mode acceptEdits

--sandbox workspace-write / acceptEdits / --dangerously-skip-permissions are there so each CLI can actually write files without blocking on an approval prompt — none of these processes have a TTY, so an unhandled prompt would hang until timeout_ms.

Edit src/registry.ts to add/remove agents or change how each one is invoked.

Tools

  • get_agent_roster — returns the roster above as JSON (id, specialization, description, resolved CLI command). Call this first to decide which agent_name fits a subtask.
  • delegate_task — spawns agent_name's CLI in workspace_path with prompt, waits for it to exit (or hit timeout_ms, default 15 min, max 1 hour), and returns { exitCode, signal, stdout, stderr, timedOut, durationMs }. Output per stream is capped at ~5MB (further output is dropped and truncated is set).

There's also a plan-and-delegate MCP prompt that spells out the intended workflow: call get_agent_roster → break the task into subtasks → confirm the plan for anything large → run delegate_task per subtask → summarize results.

Setup

npm install
npm run build

Each agent's CLI must be installed and reachable on PATH under the name in src/registry.ts (codex, agy, claude). If your local install differs, copy .env.example to .env (or set the vars in your shell / MCP client config) to point at the real binary:

MCP_SWARM_CODEX_CMD=
MCP_SWARM_AGY_CMD=
MCP_SWARM_CLAUDE_CMD=

Spawning goes through cross-spawn rather than a shell, so Windows .cmd/.bat shims (npm global installs) resolve correctly and the prompt text can't be interpreted as shell metacharacters.

Register with an MCP client

{
  "mcpServers": {
    "swarm-router": {
      "command": "node",
      "args": ["C:/KERJAAN/mcp-swarm-router/dist/index.js"]
    }
  }
}

Development

npm run dev    # run src/index.ts directly via tsx
npm run build  # compile to dist/
npm start      # run the compiled server

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选