ai-progress-monitor
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ai-progress-monitorStart reporting task progress and milestones"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI 工作台进度监控平台 (ai-progress-monitor)
🙋 拷贝话术:想接入新工作台时,把下方「话术」直接粘贴给那个 AI 即可;想接入的 AI 是 Codex/Claude/Clacky,可改用「自动配置」。详见文末「一键接入」。
统一监控多个 AI 工作台(Codex / Cursor / Claude Code / OpenCode 等)的任务进度。 各工作台通过 MCP 协议上报任务与关键节点,你在一个网页看板上实时查看, 关键节点(里程碑/成功/失败)自动弹出 macOS 系统通知横幅。
Codex ──┐
Cursor ─┤─ MCP (record_task / update_progress / log_node)
Claude ─┼──────────────────────────► mcp_server.py ─► SQLite
OpenCode┘ │
┌─────▼─────┐
FastAPI + SSE │
┌──────────┴────────┐
│ 网页看板 │
│ localhost:8777 │
└───────────────────┘🪄 直接粘贴的话术
用在任意 AI 工作台(Codex / Cursor / Claude / OpenCode / Clacky …)。只要那个工作台已挂载本 MCP(工具:
record_task、update_progress、log_node、list_tasks),把下面这段整段粘贴给它即可,它会从此每个会话自动上报:
从现在起,请在每个会话中自动通过 ai-progress-monitor MCP 上报你的任务进度,
无需我每次提醒。规则如下:
1. 会话开始、或接手一个长任务时:调用 record_task
(task_id=你所属工作台名-短序号,agent=你所属工作台名,name=任务名,stage=starting)
2. 推进到新阶段或进度变化时:调用 update_progress(progress=0-100,stage=coding/testing/deploying 等)
3. 到达重要节点时:调用 log_node(node_type=step 或 milestone,message=简短中文描述)
4. 任务完成:调用 log_node(node_type=success);失败:调用 log_node(node_type=failed)
注:milestone / success / fail 会自动触发系统通知并更新任务状态。
判断标准:不要等我要求才上报,做了实质工作就自动上报。
若 MCP 工具暂时不可用,静默跳过,不要阻塞主任务。Related MCP server: agent-comm
功能
MCP 上报:
record_task/update_progress/log_node/list_tasks四个工具实时看板:SSE 秒级刷新(带心跳保活),断线自动切前端兜底轮询,无需手动刷新
一键接入:
setup_agents.py自动给各工作台配好 MCP开机自启:LaunchAgent 开机拉起 + 崩溃自愈
节点时间线:点击任务卡片查看其完整执行节点记录
系统通知:
milestone/success/fail节点触发 macOS 横幅通知
快速开始
1. 安装依赖(推荐 uv)
uv venv .venv
uv pip install "mcp[cli]" fastmcp fastapi uvicorn或用 pip:
python -m venv .venv
source .venv/bin/activate
pip install "mcp[cli]" fastmcp fastapi uvicorn2. 启动看板
cd ai-progress-monitor
.venv/bin/python server/dashboard.py浏览器打开 http://127.0.0.1:8777
3. 一键自动配置各工作台 MCP
.venv/bin/python scripts/setup_agents.py # 探测并配置所有已安装工作台
.venv/bin/python scripts/setup_agents.py --report # 只查看安装/配置状态
.venv/bin/python scripts/setup_agents.py --codex --cursor --claude # 只配指定的工作台自动识别 codex / cursor / claude / opencode,逐个写入对应 MCP 配置
(.codex/mcp.json、.cursor/mcp.json、claude mcp add、opencode.json),
幂等执行、不重复写入。
4. macOS 开机自启(可选)
cp scripts/com.mxppxm.ai-progress-monitor.plist ~/Library/LaunchAgents/
launchctl load -w ~/Library/LaunchAgents/com.mxppxm.ai-progress-monitor.plist启动即托;KeepAlive 让服务崩溃后自动拉起。scripts/run_dashboard.sh
负责杀掉旧进程并重启。
5. 接入工作台
见 client-configs/ 下各文件的截图式配置,替换其中 /path/to/ai-progress-monitor 为你的实际路径。
工作台上报约定(写给 AI 的通用指令)
让任何接入的 AI 遵守:
长任务开始调用
record_task(task_id=工作台名-序号, agent=工作台名, name=任务名) 阶段切换调用update_progress(progress=0-100, stage=coding/testing/deploying) 重要节点调用log_node(step/milestone/success/fail,milestone 与 success/fail 会触发系统通知)
目录结构
server/
db.py SQLite 数据层
mcp_server.py MCP stdio 上报服务
dashboard.py FastAPI 看板后端 + SSE
notify.py macOS 系统通知
dashboard/
index.html 看板页面
styles.css
app.js 实时逻辑(SSE + 兜底轮询)
client-configs/ Codex/Cursor/Claude/OpenCode 接入说明
scripts/
setup_agents.py 一键自动配置各工作台 MCP
run_dashboard.sh 看板启动辅助脚本
com.mxppxm...plist macOS launchd 自启配置模板
sse_live_test.py SSE 实时推送端到端验证脚本
data/ SQLite 数据库(自动生成)Roadmap(二期可选)
桌面浮动栏
手机推送
任务聚合统计图
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