TaskWarrior▌

by awwaiid
Boost productivity and streamline projects with TaskWarrior integration—your go-to project management software for autom
Integrates with TaskWarrior to enable viewing, adding, and completing tasks, facilitating automated task management for productivity and project workflows.
best for
- / GTD practitioners using Taskwarrior
- / Command-line users managing personal tasks
- / Developers integrating task management into AI workflows
capabilities
- / Add new tasks to Taskwarrior
- / Update existing task details
- / Delete completed or unwanted tasks
- / List tasks with project and priority filtering
- / Organize tasks by projects
- / Set and modify task priorities
what it does
Connects to your local Taskwarrior installation to manage tasks directly from your AI assistant. Lets you add, update, delete, and view tasks with project organization and priority levels.
about
TaskWarrior is a community-built MCP server published by awwaiid that provides AI assistants with tools and capabilities via the Model Context Protocol. Boost productivity and streamline projects with TaskWarrior integration—your go-to project management software for autom It is categorized under productivity. This server exposes 3 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install TaskWarrior in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
TaskWarrior is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
TaskWarrior MCP Server
Node.js server implementing Model Context Protocol (MCP) for TaskWarrior operations.
<a href="https://glama.ai/mcp/servers/e8w3e1su1x"> <img width="380" height="200" src="https://glama.ai/mcp/servers/e8w3e1su1x/badge" alt="TaskWarrior Server MCP server" /> </a>Features
- View pending tasks
- Filter tasks by project and tags
- Add new tasks with descriptions, due dates, priorities, projects and tags
- Mark tasks as complete
Note: This runs your local task binary, so TaskWarrior needs to be installed and configured!
[!WARNING] This currently uses task
idwhich is an unstable identifier; taskwarrior sometimes renumbers tasks when new ones are added or removed. In the future this should be more careful, using task UUID instead.
API
Tools
-
get_next_tasks
- Get a list of all pending tasks
- Optional filters:
project: Filter by project nametags: Filter by one or more tags
-
add_task
- Add a new task to TaskWarrior
- Required:
description: Task description text
- Optional:
due: Due date (ISO timestamp)priority: Priority level ("H", "M", or "L")project: Project name (lowercase with dots)tags: Array of tags (lowercase)
-
mark_task_done
- Mark a task as completed
- Required:
identifier: Task ID or UUID
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"taskwarrior": {
"command": "npx",
"args": [
"-y",
"mcp-server-taskwarrior"
]
}
}
}
Installation
npm install -g mcp-server-taskwarrior
Make sure you have TaskWarrior (task) installed and configured on your system.
Example usage ideas:
- What are my current work tasks?
- Executes:
task project:work next
- Executes:
- TODO: Call my sister (high priority)
- Executes:
task add priority:H Call my sister
- Executes:
- OK, I've called my sister
- Executes:
task done 1
- Executes:
License
This MCP server is licensed under the MIT License. See the LICENSE file for details.
FAQ
- What is the TaskWarrior MCP server?
- TaskWarrior is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for TaskWarrior?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
TaskWarrior is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated TaskWarrior against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: TaskWarrior is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
TaskWarrior reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend TaskWarrior for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: TaskWarrior surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
TaskWarrior has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, TaskWarrior benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired TaskWarrior into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
TaskWarrior is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.