
TaskChampion MCP
io.github.GabiUp2Published by GitHub account GabiUp2v1.0.4Updated Oct 24, 2026
MCP server for Taskwarrior 3.x, TaskChampion, and Timewarrior task management.
Overview
Lets an AI assistant read, create, modify, and complete Taskwarrior 3.x tasks and Timewarrior time entries through the local task and timew CLIs.
- What you can do
- Wraps the task and timew command-line tools and exposes them as structured MCP tools, so an assistant can read, annotate, create, complete, and delete tasks and manage time entries. It ships task schema presets (minimal, gtd, scrum, kanban, and a custom example) that describe your task structure to the model. A first-run onboarding flow sets a role and schema, and tools include get_runtime_capabilities, get_initialization_status, propose_initialization_options, use_preset_schema, save_initial_schema, and reload_configuration.
- When to use it
- Use it when you already manage tasks and time in Taskwarrior 3.x or Timewarrior and want an assistant to help with project planning, task decomposition, and time tracking without handing over your task database. It suits terminal-centric workflows on a local machine.
- Requirements
- Local process over stdio, installed from PyPI as taskchampion-mcp and run with uvx or pip. Needs Python 3.10+, Taskwarrior 3.x on PATH, and optionally Timewarrior for time tracking. A config file at ~/.config/taskchampion-mcp/config.toml must set role and schema or schema_path before operational tools work. No authentication, environment variables, or headers are declared.
Installation
In SourceWeft
- Open TaskChampion MCP in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
TaskChampion MCP
A Model Context Protocol server for Taskwarrior 3.x, TaskChampion, and Timewarrior.
For the bearded Unix jockeys and keyboard cowboys who manage their life from the terminal — and now want their LLM to lend a hand. 🧔⌨️
[PyPI version] [GitHub release] [License] [Python] [MCP Registry] [Glama]
Latest release: v1.0.4 — pip install taskchampion-mcp / uvx taskchampion-mcp
What Is This?
TaskChampion MCP is a Model Context Protocol server that lets LLMs read, create, modify, and manage your Taskwarrior tasks and Timewarrior time entries. It wraps the task and timew CLI tools and exposes them as structured MCP tools that any compatible AI assistant can call.
Why? The author self-hosts TaskChampion on a home server and wanted a clean way for LLMs to cooperate on project planning, task decomposition, and time tracking — without giving up control of the task database.
Supported Platforms
Requirements:
- Python 3.10+
- Taskwarrior 3.x (TaskChampion sync)
- Timewarrior (optional, for time tracking features)
Quick Install
Then configure your IDE's MCP settings to use:
Per-target install guides: Claude Desktop | Windsurf | Cursor | Neovim
First Run
On a fresh install with no config.toml, the server boots in onboarding mode. All tools are visible in tools/list, but operational tools return structured schema_unset errors until onboarding completes (ADR 19). You finish onboarding by persisting two keys in ~/.config/taskchampion-mcp/config.toml:
role— what the LLM can do (CONTRIBUTOR / GENERATOR / MANAGER)schemaorschema_path— which task schema the server validates against
Three ways to get there:
- Let the LLM walk you through it. Connect your IDE to the MCP server with no config and ask: "Help me set up TaskChampion MCP." The LLM calls
get_runtime_capabilitiesfirst, seesmode: "onboarding", then usesget_initialization_status->propose_initialization_options->use_preset_schema(orsave_initial_schema). The server auto-reloads on success -- no restart needed. - Run the CLI wizard:
./dev.sh init(interactive) or./dev.sh init --preset gtd --role CONTRIBUTOR --non-interactive(scripted). - Edit
config.tomlby hand -- see quick_start.md. Two keys, then callreload_configurationfrom the LLM (or restart the IDE).
The three paths are interchangeable and produce identical state. Pick by who should be doing the typing -- see initialization_flows.md for the decision guide.
Permission Levels
Control what the LLM can do with your tasks via three cumulative roles:
Set the role in ~/.config/taskchampion-mcp/config.toml:
Task Schemas
Taskwarrior supports custom workflows via UDAs (User Defined Attributes). TaskChampion MCP ships with schema presets that teach the LLM your task structure:
On first run, the MCP will prompt you to select a schema or auto-generate one from your existing tasks.
Security
This tool gives an LLM indirect access to your task management CLI. Security is not optional:
- No shell execution — all CLI calls use subprocess argument lists, never
shell=True - Input sanitization — all LLM inputs validated against allowlists before passing to CLI
- Rate limiting — configurable per-minute/per-hour caps prevent runaway loops
- Audit logging — every operation logged with timestamp, tool, parameters, result, and
result_code - Code-tagged envelopes — every tool response includes a stable
codefield for machine-safe branching - Dry-run mode — every destructive operation supports
dry_runpreview without mutation - Confirmation mode — lifecycle operations use explicit confirmation tokens when confirmation is enabled
- Sensitive field redaction — configurable fields hidden from LLM responses
See ADR 9, ADR 13, and ADR 14 for the full security and observability design.
Taskwarrior Compatibility
We focus on the modern Taskwarrior 3.x + TaskChampion stack. Taskserver (taskd) is deprecated and will receive limited support in a future version. See ADR 8.
Documentation
Troubleshooting
Common first-run and config issues. Detailed walkthroughs live in docs/manuals/quick_start.md and docs/manuals/initialization_flows.md.
For deeper failure modes, every MCP tool returns a stable error_code field (ADR 14) and every call is audit-logged (ADR 13) at ~/.local/share/taskchampion-mcp/audit.log by default.
Contributing
See docs/CONTRIBUTING.md for the full guide. Key points:
- Branch from
dev, PR toqa, release fromqatomain - Semantic versioning (
vMAJOR.MINOR.PATCH) - LLM-assisted contributions must be attributed (see
docs/llm_context/AGENTS.md) - All unverified assumptions must be logged in
docs/llm_context/assumptions_and_ideas.md
License
Apache License 2.0 — use freely for private and commercial purposes. Attribution required via the NOTICE file.
Copyright 2026 gabiup2
This project was bootstrapped with assistance from Claude claude-sonnet-4-20250514 via Windsurf Cascade.
Source: README.md at commit f5af773
Tools
0Version history
1- v1.0.4LatestSep 16, 2026
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