@GabiUp2
基本信息
TaskChampion MCP Server
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"taskchampion": {
"command": "uvx",
"args": [
"taskchampion-mcp"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
TaskChampion MCP is a Model Context Protocol server that lets LLMs read, create, modify, and manage Taskwarrior 3.x tasks and Timewarrior time entries. It wraps the task and timew CLI tools and exposes them as structured MCP tools for any compatible AI assistant. It is for users who self-host TaskChampion and want LLMs to help with project planning, task decomposition, and time tracking.
Install via pip install taskchampion-mcp or uv tool install taskchampion-mcp, then configure your IDE’s MCP settings to use the command taskchampion-mcp-server with no arguments. On first run, the server boots in onboarding mode; you must set role and schema/schema_path in ~/.config/taskchampion-mcp/config.toml — either through an LLM-driven walkthrough, the CLI wizard (./dev.sh init), or by hand-editing the config.
On a fresh install with no config, the server boots in onboarding mode. All tools are visible in `tools/list`, but operational tools return structured `schema_unset` errors until you set `role` and `schema`/`schema_path` in `~/.config/taskchampion-mcp/config.toml`.
Set `role` (CONTRIBUTOR / GENERATOR / MANAGER) and `schema` or `schema_path` under the `[server]` section in `config.toml`. You can use the LLM-driven initialization process, the CLI wizard (`./dev.sh init`), or hand-edit the file and then call `reload_configuration`.
Three cumulative roles: CONTRIBUTOR (read, annotate, modify), GENERATOR (plus create), and MANAGER (plus complete and delete). Set the role in `~/.config/taskchampion-mcp/config.toml`.
If tools return `schema_unset` errors, `~/.config/taskchampion-mcp/config.toml` is missing either `role` or `schema`/`schema_path`. Add both and call `reload_configuration` or restart the MCP server.
Yes. All CLI calls use subprocess argument lists (never `shell=True`), LLM inputs are sanitized against allowlists, configurable rate limiting caps requests per minute/hour, every operation is audit-logged, and destructive actions support dry-run previews and confirmation tokens.
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