agent-coordination-mcp
Experimental MCP server for coordinating CLI agents across projects using file-based task boards and assignment tracking.
README
agent-coordination-mcp
An experimental local MCP server for coordinating installed CLI agents across projects that use file-based task boards, locks, and shared status files.
The goal is not to replace the ai-agent-teamwork workflow. It is to expose that workflow as a small MCP control plane so a primary agent can see which CLI agents are available, assign work, inspect project coordination state, and keep durable assignment records.
Why This Works
The workflow works if the MCP server stays narrow:
- MCP is the coordination surface, not the editor.
- Project state remains in plain files owned by each project.
- CLI agents continue to run as separate tools with their own approval and sandbox behavior.
- Assignment and heartbeat records are explicit JSON state, not inferred from hidden sessions.
The risky part is supervising long-running CLI processes. This first slice records assignments and generates dispatch intent; later slices can add process launch adapters per CLI once the approval and lifecycle model is clear.
Current Tools
list_cli_agents- detect known local CLI agents onPATH.get_project_status- summarize.agent-tasks.json,.agent-manifest.json, and.agent-status.mdfor a project.list_assignments- read active and historical assignment records.assign_task- record a task assignment to a detected CLI agent.update_assignment_status- update assignment status and notes.
Install
uv sync
uv run agent-coordination-mcp
Dynamic MCP Proxy Entry
Add this to /home/stephen/dynamic-mcp-proxy-server/user.catalogue.json:
{
"name": "agent-coordination",
"description": "Local MCP control plane for detecting CLI agents and coordinating file-based project task boards",
"command": "uv --project /home/stephen/projects/agent-coordination-mcp run agent-coordination-mcp",
"tags": ["agents", "coordination", "mcp", "cli", "local"],
"tech_stack": ["python", "mcp", "cli-agents"],
"runtime": "stdio",
"env_vars": []
}
Planned Slices
- Inventory and assignment tracking.
ai-agent-teamworktask board adapters.- CLI-specific dispatch adapters for
opencode,codex,gemini,claude, and other installed agents. - Process/session tracking where supported by the CLI.
- Dynamic proxy integration and research tools such as
devto-mcp-server.
See docs/ROADMAP.md for the current implementation plan. The next slice is capability-aware inventory before automated dispatch.
Non-Goals
- No hidden project edits by the MCP server.
- No generic shell execution tool.
- No automatic force-unlocking or stale-task rewrites without explicit tool calls.
- No assumption that every CLI supports resumable sessions.
推荐服务器
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。
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。
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。
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。
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。
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。
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。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。