Task Hub MCP
Allows Hermes agents to share task state via Task Hub MCP server, enabling cross-session context persistence and collaboration.
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., "@Task Hub MCPinitialize a new task for 'API redesign'"
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.
Task Hub MCP
跨 AI agent 的本地任务上下文共享。Task Hub 是一个 stdio MCP server,让 Codex、Claude Code、Hermes、VS Code 等客户端读写同一份任务状态。
当你切换 agent 时,新 agent 可以直接加载任务目标、当前进度、关键决策、踩坑记录和文件快照,不必重新翻完整对话。
核心设计
meta.json是任务状态的唯一真源。context.md是从meta.json生成的蒸馏上下文,加载时会自动修复。conversation.jsonl保存调用方显式传入的对话消息。revision防止旧 agent 覆盖其他 agent 刚保存的新状态。MCP server 只维护一套接口,不为不同客户端复制业务逻辑。
Related MCP server: Agent Switchboard
六个工具
工具 | 用途 |
| 创建并开始追踪任务 |
| 保存上下文、进度、决策、踩坑、文件和可选对话 |
| 加载指定任务及其当前 revision |
| 按状态或标签列出任务 |
| 加载 active 任务或恢复 paused 任务 |
| 暂停任务,或用 |
安装
要求 Python 3.10 或更高版本。
git clone https://github.com/zjuphD/task-hub-mcp.git
cd task-hub-mcp
python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install .开发时使用 editable install:
.venv/bin/python -m pip install -e .安装后确认命令可用:
.venv/bin/task-hub-mcpserver 使用 stdio,直接启动后安静等待 MCP client 连接属于正常行为。
Windows 虚拟环境中的可执行文件通常位于 .venv\\Scripts\\task-hub-mcp.exe。
配置 MCP client
以下示例使用安装后的命令。请把 /absolute/path/to/task-hub-mcp 替换为仓库的真实绝对路径。
Codex
在 ~/.codex/config.toml 中添加:
[mcp_servers.task-hub]
command = "/absolute/path/to/task-hub-mcp/.venv/bin/task-hub-mcp"
startup_timeout_sec = 30Hermes
在 ~/.hermes/config.yaml 中添加:
mcp_servers:
task-hub:
enabled: true
command: /absolute/path/to/task-hub-mcp/.venv/bin/task-hub-mcp
timeout: 120
connect_timeout: 60检查连接:
hermes mcp list
hermes mcp test task-hubHermes 模型侧的工具名通常带 server 前缀,例如 mcp_task_hub_task_save。
Claude Code / VS Code
在 .mcp.json 中添加:
{
"mcpServers": {
"task-hub": {
"command": "/absolute/path/to/task-hub-mcp/.venv/bin/task-hub-mcp"
}
}
}不同 MCP client 的配置文件位置可能不同,但启动命令相同。
存储结构
默认存储目录:
~/.task-hub/tasks/每个任务目录:
~/.task-hub/tasks/<task-id>/
├── meta.json
├── context.md
├── conversation.jsonl
└── artifacts/可通过环境变量覆盖任务目录:
TASK_HUB_TASKS_DIR=/path/to/tasks如果 MCP client 需要传递环境变量,可以把它放在对应 server 的 env 配置中。
Revision 冲突保护
task_init、task_load、task_save、task_resume 和 task_stop 都会返回当前 revision。
保存前把加载时拿到的 revision 作为 expected_revision 传回:
{
"name": "release-v1",
"context": "当前蒸馏状态",
"expected_revision": 3,
"agent": "codex"
}如果其他 agent 已经把任务更新到 revision 4,这次保存不会产生文件或对话副作用,而是返回:
{
"success": false,
"code": "revision_conflict",
"expected_revision": 3,
"actual_revision": 4,
"hint": "Load the latest task state, merge your changes, and save again."
}兼容旧客户端时可以不传 expected_revision,此时保持最后写入者覆盖的行为。跨 agent 工作流建议始终传入。
工具参数
task_init
{
"name": "任务名",
"description": "任务目标,可选",
"tags": ["可选标签"]
}task_save
{
"name": "任务名或 task id",
"context": "当前蒸馏状态",
"progress": "本次进度,可选",
"decisions": ["新决策,可选"],
"pitfalls": ["新踩坑,可选"],
"artifacts": [
{
"path": "/absolute/path/to/file",
"description": "文件说明"
}
],
"agent": "codex",
"messages": [
{
"role": "user",
"content": "用户消息"
}
],
"expected_revision": 1
}MCP server 无法自动读取 agent 私有会话窗口。只有调用方显式传入 messages 时,消息才会写入 conversation.jsonl。
部分 MCP client 会把数组包装成 {"item": [...]}、{"items": [...]} 或 {"value": [...]};server 会自动归一化这些输入。
task_load
{
"name": "任务名或 task id",
"include_conversation": false
}task_list
{
"status": "active",
"tag": "可选标签"
}status 可选值为 active、paused、archived。
task_resume
{
"name": "可选任务名或 task id"
}指定 active 任务时直接加载。
指定 paused 任务时恢复为 active。
不指定任务时优先加载最近的 active 任务;如果没有 active 任务,则恢复最近的 paused 任务。
archived 任务不能恢复。
task_stop
{
"name": "任务名或 task id",
"reason": "停止原因,可选",
"archive": false,
"expected_revision": 2
}默认状态变为 paused;archive=true 时状态变为 archived。
Artifact 限制
默认单个 artifact 最大为 50 MiB。超限文件会跳过并作为 warning 返回。
TASK_HUB_MAX_ARTIFACT_BYTES=104857600设为 0 可以关闭大小限制。还可以限制允许读取的目录,多个目录使用操作系统路径分隔符连接:
TASK_HUB_ALLOWED_ARTIFACT_ROOTS=/workspace/project:/workspace/results启用允许目录后,符号链接也会按解析后的真实路径检查。
可靠性与隐私
meta.json和context.md使用临时文件、fsync和原子替换。context.md发生缺失或过期时,task_load会从meta.json自动重建。Unix 系统使用文件锁串行化同一任务的写入;创建任务使用全局锁避免同名竞争。
revision解决文件锁无法发现的语义级旧状态覆盖。artifact 同名时自动生成唯一文件名。
conversation.jsonl的坏行会被标记并跳过,不会导致整个任务加载失败。
Task Hub 以当前用户权限运行。保存的上下文、对话和 artifact 可能包含源代码、绝对路径、密钥或其他隐私数据;不要把 ~/.task-hub/tasks/ 直接提交到公开仓库。对不完全信任的 agent,建议配置 TASK_HUB_ALLOWED_ARTIFACT_ROOTS。
开发验证
python -m compileall -q task_hub_mcp tests
python -m unittest discover -s tests -v
python -m pip wheel --no-deps . -w distMCP 握手验证:
import asyncio
from pathlib import Path
from mcp import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client
async def main():
executable = Path(".venv/bin/task-hub-mcp").resolve()
params = StdioServerParameters(command=str(executable))
async with stdio_client(params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
tools = await session.list_tools()
print([tool.name for tool in tools.tools])
asyncio.run(main())预期工具列表:
task_init, task_save, task_load, task_list, task_resume, task_stopLicense
MIT
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