任务编排器 是 AI Skill Hub 本期精选MCP工具之一。综合评分 8.2 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
基于MCP协议的开源工具,为AI代理提供服务端强制的工作流纪律和持久化管理能力。支持工作流编排、内存管理和任务调度,适合构建可靠的AI助手和自动化系统的开发者。
任务编排器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
基于MCP协议的开源工具,为AI代理提供服务端强制的工作流纪律和持久化管理能力。支持工作流编排、内存管理和任务调度,适合构建可靠的AI助手和自动化系统的开发者。
任务编排器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/jpicklyk/task-orchestrator
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"-----": {
"command": "npx",
"args": ["-y", "task-orchestrator"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 任务编排器 执行以下任务... Claude: [自动调用 任务编排器 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"_____": {
"command": "npx",
"args": ["-y", "task-orchestrator"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Server-enforced workflow discipline for AI agents.
Prompt-based frameworks hope the LLM follows instructions. This one blocks the call if it doesn't.
---
Prerequisite: Docker installed and running.
work_item_schemas: feature-task: notes: - key: requirements role: queue required: true description: "Acceptance criteria before starting" guidance: "Cover: problem statement, acceptance criteria, alternatives considered, test strategy." skill: "spec-quality" - key: implementation-notes role: work required: true description: "What was built and why" ```
advance_item(trigger="start") from queue requires requirements to be filled. No exceptions, no prompt-dependent compliance — the server returns an error with exactly which notes are missing.
The guidance field provides authoring instructions surfaced at the right moment — when the agent is about to fill that note, get_context returns the guidance as a guidancePointer. The skill field takes this further: it references a specific skill that the agent must invoke before filling the note, providing a deterministic evaluation framework rather than freeform prose. Together, they create structured agent behavior that's configured in YAML, not hardcoded in prompts.
Mount your project's config to activate gate enforcement:
{
"mcpServers": {
"mcp-task-orchestrator": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-v", "mcp-task-data:/app/data",
"-v", "${workspaceFolder}/.taskorchestrator:/project/.taskorchestrator:ro",
"-e", "AGENT_CONFIG_DIR=/project",
"ghcr.io/jpicklyk/task-orchestrator:latest"
]
}
}
}
Without schemas, all 14 tools work in schema-free mode — no gates, no required notes. Add schemas when you want enforcement.
---
Schemas define what agents must produce at each phase — and the server blocks progression until it's done. But schemas do more than gate transitions. They set a planning floor: when an agent enters plan mode, the schema tells it what documentation must exist before implementation can start, shaping the plan structure itself.
```yaml
The plugin adds workflow automation on top of the MCP server — skills, hooks, and an orchestrator output style.
Install:
/plugin marketplace add https://github.com/jpicklyk/task-orchestrator
/plugin install task-orchestrator@task-orchestrator-marketplace
What it adds:
| Layer | What it does |
|---|---|
| **Skills** | Slash commands for common workflows — /task-orchestrator:create-item, /task-orchestrator:manage-schemas, /task-orchestrator:quick-start |
| **Hooks** | Automatic context injection at session start, plan mode integration, sub-agent context handoff, actor attribution enforcement |
| **Output style** | Workflow Orchestrator mode — Claude plans, delegates to sub-agents, and tracks progress without writing code directly |
The MCP server works without the plugin. The plugin makes it seamless with Claude Code.
---
设计理念先进,针对AI代理的工作流管制提供系统解决方案。维护活跃,Kotlin语言保证代码质量,适合企业级应用。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,任务编排器 在MCP工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | task-orchestrator |
| 原始描述 | 开源MCP工具:Server-enforced workflow discipline for AI agents. An MCP server providing persi。⭐185 · Kotlin |
| Topics | 工作流编排AI代理KotlinMCP服务器任务管理持久化 |
| GitHub | https://github.com/jpicklyk/task-orchestrator |
| License | MIT |
| 语言 | Kotlin |
收录时间:2026-05-20 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端