productivity

Tasks

flesler

by flesler

Tasks offers project tracking software and time management tools using the Eisenhower Matrix across Markdown, JSON, and

Provides task management capabilities across Markdown, JSON, and YAML files with tools for creating, searching, updating, and summarizing tasks while automatically managing work-in-progress status transitions.

github stars

39

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Only 5 tools to reduce AI confusionBatch operations to minimize LLM costsSupports Markdown, JSON, and YAML formats

best for

  • / AI assistants managing project tasks
  • / Developers tracking work across different file formats
  • / Teams wanting efficient task management with minimal API calls

capabilities

  • / Create tasks in multiple file formats
  • / Search tasks by status, text, or ID
  • / Update task statuses in bulk
  • / Track work-in-progress automatically
  • / Generate task summaries by status

what it does

Manages tasks across Markdown, JSON, and YAML files with search, creation, and status tracking capabilities. Designed to minimize AI tool confusion and LLM API costs through batch operations.

about

Tasks is a community-built MCP server published by flesler that provides AI assistants with tools and capabilities via the Model Context Protocol. Tasks offers project tracking software and time management tools using the Eisenhower Matrix across Markdown, JSON, and It is categorized under productivity. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Tasks 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

Tasks is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP Tasks 📋

Install MCP Server npm version Node.js License: MIT Docker

An efficient task manager. Designed to minimize tool confusion and maximize LLM budget efficiency while providing powerful search, filtering, and organization capabilities across multiple file formats (Markdown, JSON, YAML)

📚 Table of Contents

Features

  • Ultra-efficient design: Minimal tool count (5 tools) to reduce AI confusion
  • 🎯 Budget-optimized: Batch operations, smart defaults and auto-operations minimize LLM API calls
  • 🚀 Multi-format support: Markdown (.md), JSON (.json), and YAML (.yml) task files
  • 🔍 Powerful search: Case-insensitive text/status filtering with OR logic, and ID-based lookup
  • 📊 Smart organization: Status-based filtering with customizable workflow states
  • 🎯 Position-based indexing: Easy task ordering with 0-based insertion
  • 📁 Multi-source support: Manage multiple task files simultaneously
  • 🔄 Real-time updates: Changes persist automatically to your chosen format
  • 🤖 Auto WIP management: Automatically manages work-in-progress task limits
  • 🚫 Duplicate prevention: Automatically prevents duplicate tasks
  • 🛡️ Type-safe: Full TypeScript support with Zod validation
  • 🔒 Ultra-safe: AI has no way to rewrite or delete your tasks (unless you enable it), only add and move them
  • 📅 Optional reminders: Enable a dedicated Reminders section the AI constantly sees and can maintain

🚀 Quick Start

Add this to ~/.cursor/mcp.json for Cursor, ~/.config/claude_desktop_config.json for Claude Desktop.

Option 1: NPX (Recommended)

{
  "mcpServers": {
    "mcp-tasks": {
      "command": "npx",
      "args": ["-y", "mcp-tasks"]
    }
  }
}

Option 2: Docker

{
  "mcpServers": {
    "mcp-tasks": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "flesler/mcp-tasks"
      ]
    }
  }
}

🤖 AI Integration Tips

To encourage the AI to use these tools, you can start with a prompt like the following, with any path you want with .md (recommended), .json, .yml:

Use mcp-tasks tools to track our work in path/to/tasks.md

If you are telling it about new or updated tasks, you can append this to the end of your prompt:

use mcp-tasks

Adding tasks while AI works: To safely add tasks without interfering with AI operations, use the CLI from a separate terminal:

npx mcp-tasks add "Your new task text" "To Do" 0

🔧 Installation Examples

Full configuration with custom environment:

{
  "mcpServers": {
    "mcp-tasks": {
      "command": "npx",
      "args": ["-y", "mcp-tasks"],
      "env": {
        "STATUS_WIP": "In Progress",
        "STATUS_TODO": "To Do",
        "STATUS_DONE": "Done",
        "STATUS_REMINDERS": "Reminders",
        "STATUS_NOTES": "Notes",
        "STATUSES": "In Progress,To Do,Done,Backlog,Reminders,Notes",
        "AUTO_WIP": "true",
        "PREFIX_TOOLS": "true",
        "KEEP_DELETED": "true",
        "TRANSPORT": "stdio",
        "PORT": "4680",
        "INSTRUCTIONS": "Use mcp-tasks tools when the user mentions new or updated tasks"
      }
    }
  }
}

HTTP transport for remote access:

First run the server:

TRANSPORT=http PORT=4680 npx mcp-tasks

Then:

{
  "mcpServers": {
    "mcp-tasks": {
      "type": "streamableHttp",
      "url": "http://localhost:4680/mcp"
    }
  }
}

📁 Supported File Formats

ExtensionFormatBest ForAuto-Created
.mdMarkdownHuman-readable task lists
.jsonJSONStructured data, APIs
.ymlYAMLConfiguration files

Format is auto-detected from file extension. All formats support the same features and can be mixed in the same project.

Recommended: Markdown (.md) for human readability and editing

⚠️ Warning: Start with a new file rather than using pre-existing task files to avoid losing non-task content.

🛠️ Available Tools

When PREFIX_TOOLS=true (default), all tools are prefixed with tasks_:

ToolDescriptionParameters
tasks_setupInitialize a task file (creates if missing, supports .md, .json, .yml)source_path, workspace?
tasks_searchSearch tasks with filteringsource_id, statuses?, terms?, ids?
tasks_addAdd new tasks to a statussource_id, texts[], status, index?
tasks_updateUpdate tasks by IDsource_id, ids[], status, index?
tasks_summaryGet task counts and work-in-progresssource_id

ID Format: Both source_id (from file path) and task id (from task text) are 4-character alphanumeric strings (e.g., "xK8p", "m3Qw").

Tool Examples

Setup a task file:

tasks_setup({
  workspace: "/path/to/project",
  source_path: "tasks.md"  // relative to workspace or absolute
  // source_path: "tasks.json"
  // source_path: "tasks.yml"
})
// Returns: {"source":{"id":"xK8p","path":"/path/to/project/tasks.md"},"Backlog":0,"To Do":0,"In Progress":0,"Done":0,"inProgress":[]}
// Source ID (4-char alphanumeric) is used for all subsequent operations

Add tasks:

tasks_add({
  source_id: "xK8p", // From setup response
  texts: ["Implement authentication", "Write tests"],
  status: "To Do",
  index: 0  // Add at top (optional)
})
// Returns: {"source":{"id":"xK8p","path":"/absolute/path/to/tasks.md"},"Backlog":0,"To Do":2,"In Progress":0,"Done":0,"inProgress":[],"tasks":[{"id":"m3Qw","text":"Implement authentication","status":"To Do","index":0},{"id":"p9Lx","text":"Write tests","status":"To Do","index":1}]}

Search and filter:

tasks_search({
  source_id: "xK8p",        // From setup response
  terms: ["auth", "deploy"],          // Search terms (text or status, OR logic)
  statuses: ["To Do"],      // Filter by status
  ids: ["m3Qw", "p9Lx"]     // Filter by specific task IDs
})
// Returns: [{"id":"m3Qw","text":"Implement authentication","status":"To Do","index":0}]

Update tasks status:

tasks_update({
  source_id: "xK8p",        // From setup response
  ids: ["m3Qw", "p9Lx"],    // Task IDs from add/search responses
  status: "Done"            // Use "Deleted" to remove
})
// Returns: {"source":{"id":"xK8p","path":"/absolute/path/to/tasks.md"},"Backlog":0,"To Do":0,"In Progress":0,"Done":2,"inProgress":[],"tasks":[{"id":"m3Qw","text":"Implement authentication","status":"Done","index":0},{"id":"p9Lx","text":"Write tests","status":"Done","index":1}]}

Get overview:

tasks_summary({
  source_id: "xK8p"         // From setup response
})
// Returns: {"source":{"id":"xK8p","path":"/absolute/path/to/tasks.md"},"Backlog":0,"To Do":0,"In Progress":1,"Done":2,"inProgress":[{"id":"r7Km","text":"Fix critical bug","status":"In Progress","index":0}]}

🎛️ Environment Variables

VariableDefaultDescription
TRANSPORTstdioTransport mode: stdio or http
PORT4680HTTP server port (when TRANSPORT=http)
PREFIX_TOOLStruePrefix tool names with tasks_
STATUS_WIPIn ProgressWork-in-progress status name
STATUS_TODOTo DoToDo status name
STATUS_DONEDoneCompleted status name
STATUS_REMINDERSRemindersReminders for the AI (empty string to disable)
STATUS_NOTESNotesNotes/non-actionable tasks (empty string to disable)
STATUSESBacklogComma-separated additional statuses
AUTO_WIPtrueOne WIP moves rest to To Do, first To Do to WIP when no WIP's
KEEP_DELETEDtrueRetain deleted tasks (AI can't lose you tasks!)
INSTRUCTIONS...Included in all tool responses, for the AI to follow
SOURCES_PATH./sources.jsonFile to store source registry (internal)
DEBUGfalseif true, enable the tasks_debug tool

Advanced Configuration Examples

Optional, the WIP/ToDo/Done statuses can be included to control their order.

Custom workflow statuses:

{
  "env": {
    "STATUSES": "WIP,Pending,Archived,Done,To Review",
    "STATUS_WIP": "WIP",
    "STATUS_TODO": "Pending",
    "AUTO_WIP": "false"
  }
}

📊 File Formats

Markdown (.md) - Human-Readable

# Tasks - File Name

## In Progress
- [ ] Write user registration

## To Do
- [ ] Implement authentication
- [ ] Set up CI/CD pipeline

## Backlog
- [ ] Plan architecture
- [ ] Design database schema

## Done
- [x] Set up project structure
- [x] Initialize repository

## R

---

FAQ

What is the Tasks MCP server?
Tasks 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 Tasks?
This profile displays 31 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 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. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 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.

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Ratings

4.731 reviews
  • Shikha Mishra· Dec 28, 2024

    Useful MCP listing: Tasks is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Ganesh Mohane· Dec 24, 2024

    Tasks has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Isabella Shah· Dec 16, 2024

    Strong directory entry: Tasks surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Sakshi Patil· Nov 15, 2024

    According to our notes, Tasks benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Zara Garcia· Nov 7, 2024

    I recommend Tasks for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Valentina Park· Nov 7, 2024

    Tasks is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Aisha Gill· Oct 26, 2024

    Tasks reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Isabella Haddad· Oct 26, 2024

    Useful MCP listing: Tasks is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Chaitanya Patil· Oct 6, 2024

    We wired Tasks into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Aisha Nasser· Sep 5, 2024

    We wired Tasks into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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