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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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 32 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.6★★★★★32 reviews- ★★★★★Charlotte Bansal· Dec 24, 2024
TaskWarrior is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Charlotte Menon· Dec 16, 2024
TaskWarrior has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Neel Desai· Dec 8, 2024
Useful MCP listing: TaskWarrior is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Pratham Ware· Dec 4, 2024
TaskWarrior is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Min Sanchez· Nov 27, 2024
Strong directory entry: TaskWarrior surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Sakshi Patil· Nov 23, 2024
TaskWarrior is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Camila Perez· Nov 15, 2024
TaskWarrior is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Neel Ndlovu· Oct 18, 2024
I recommend TaskWarrior for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Chaitanya Patil· Oct 14, 2024
We evaluated TaskWarrior against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Henry Farah· Oct 6, 2024
We evaluated TaskWarrior against two servers with overlapping tools; this profile had the clearer scope statement.
showing 1-10 of 32