Part of our Developer Tools MCP category.

At a glance: 26,600 GitHub stars, 2,500 forks, 1,214 commits, last commit April 2026, npm v0.43.1, 36 tools (in three loading tiers), MIT license with Commons Clause, ~30,500 npm downloads/week, PulseMCP ~1.7M all-time visitors (#30 globally, ~31,600 weekly). Created by Eyal Toledano, Ralph Krysler, and Jason Zhou. Launched March 2025.

Task Master is the breakout hit of the MCP ecosystem. In just over a year it went from zero to 26,600 GitHub stars — making it one of the most starred MCP servers in existence. The pitch is simple: give your AI coding agent a structured project manager. Feed it a PRD, get back a dependency-ordered task list, then let the agent work through tasks one at a time with full context awareness.

Built by Eyal Toledano and maintained at eyaltoledano/claude-task-master, it’s designed to slot into AI editors like Cursor, Windsurf, Claude Code, Lovable, and Roo Code. The npm package is task-master-ai, and there’s also a companion platform called Hamster for team collaboration built on top.

What It Does

Task Master exposes up to 36 tools organized in three configurable loading tiers to manage context window consumption:

Core Tools (7 tools, ~5K tokens) — the minimum for task execution:

  • get_tasks — list all tasks with status and metadata
  • next_task — get the next actionable task based on dependencies and status
  • get_task — get details for a specific task
  • set_task_status — update task status (pending, in-progress, done, etc.)
  • update_subtask — update subtask details and status
  • parse_prd — parse a PRD document into structured tasks with dependencies
  • expand_task — break a task into detailed subtasks using AI analysis

Standard Tools (15 tools, ~10K tokens) — adds project setup and analysis:

  • All core tools plus initialize_project, analyze_project_complexity, expand_all, add_subtask, remove_task, generate, add_task, complexity_report

All Tools (36 tools, ~21K tokens) — the full suite including dependencies, tags, research, and advanced management

The tiered loading approach is genuinely thoughtful. MCP servers with dozens of tools can consume significant context window space just from tool descriptions. Letting users choose “core” (~5K tokens) vs “all” (~21K tokens) means the server scales with project complexity rather than always imposing maximum overhead.

Multi-model support is another standout feature. You configure three model slots — main, research, and fallback — and can use providers including Anthropic (Claude), OpenAI, Google Gemini, Perplexity, and others. If your main model fails, the fallback kicks in automatically. The research model can pull fresh information via Perplexity for context-aware task planning.

The Workflow

The typical flow:

  1. Initialize a project with initialize_project
  2. Parse a PRD — feed it a requirements document, get back a structured task list with numbered tasks, dependencies, priorities, and complexity scores
  3. Analyze complexity — get a report on which tasks are hardest, where risks cluster
  4. Work through tasks — use next_task to get the next unblocked task, implement it, mark it done
  5. Expand tasks — break complex tasks into subtasks as you go
  6. Research — use the research model to pull in fresh context when needed

Tasks are stored in a local tasks.json file (or optionally in Supabase for persistence). Each task gets an ID, title, description, dependencies, status, priority, complexity score, and optional subtasks. Tags let you organize tasks into phases or categories.

Setup

One-click install for Cursor 1.0+ (via deeplink) or manual configuration:

{
  "mcpServers": {
    "taskmaster-ai": {
      "command": "npx",
      "args": ["-y", "task-master-ai@latest"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key-here"
      }
    }
  }
}

For Claude Code, no API key is needed — it uses the Claude Code CLI as the provider directly.

Configuration lives in .taskmaster/config.json per project, where you set model providers, tool loading mode, and other preferences.

What’s Good

Explosive adoption for a reason. 26,600 stars in ~13 months isn’t hype alone. The core idea — giving AI agents structured task management rather than letting them freestyle through a codebase — addresses a real pain point. Developers using Cursor or Claude Code report measurably fewer “AI wanders off track” incidents when tasks are pre-structured with dependencies.

Tiered tool loading. The three-tier system (core/standard/all) is a design pattern more MCP servers should adopt. Context window space is precious, and most sessions only need 7 tools, not 36.

Multi-model flexibility. Being able to use Claude for main work, Perplexity for research, and GPT as fallback means you’re not locked into one provider. Runtime configuration via MCP tools means you can switch models without restarting.

PRD parsing is the killer feature. Taking a natural-language requirements document and producing a dependency-ordered, complexity-scored task list is genuinely useful. It turns vague “build me X” instructions into structured work plans.

Active development. 1,214 commits, regular releases (roughly every 2-3 weeks), responsive issue triage with priority labels. The project isn’t abandonware.

Claude Code integration. The ability to use Claude Code as a provider (no separate API key needed) lowers the barrier to entry significantly.

What’s Not

Sentry telemetry captures full AI prompts and responses by default. Issue #1681 (April 4, 2026, high-priority, still open) reports that the default Sentry configuration uses sendDefaultPii: true and recordInputs: true, recordOutputs: true on the Vercel AI SDK integration, capturing 100% of AI prompts and responses. This means your PRDs, task descriptions, implementation details, and AI-generated code are sent to Sentry by default. The opt-out mechanism is fragile — if .taskmaster/config.json isn’t found in the working directory at MCP server start time, telemetry defaults to fully enabled. This is a significant privacy concern for anyone processing proprietary code or business requirements through Task Master.

149 open issues with data integrity bugs. Issue #1683 reports task status drift between the MCP state and tasks.json. Issue #1567 documents race conditions when multiple Claude Code windows write to tasks.json simultaneously — work can be silently lost. Issue #1647 reports task ID collisions when moving tasks between tags. For a task management tool, data integrity bugs are existential.

MIT with Commons Clause is not open source. The Commons Clause restricts selling Task Master itself, offering it as a SaaS, or building competing products from the code. While personal and commercial use is permitted, this is a meaningful restriction that disqualifies it from the OSI definition of open source. Forks exist (James-Cherished-Inc/AI-task-master, kylantomita/task-master-ai) but their legal standing under Commons Clause is ambiguous.

No release in 3 weeks. The latest npm release is v0.43.1 from March 31, 2026 (as of this review on April 20). While the repo is active with commits, the gap between repo activity and published releases means users on the npm version may be missing fixes for known issues.

Claude Code integration issues. Issues #1039, #963, and #784 document problems with Task Master failing to work properly with Claude Code — ironic for a tool originally named “claude-task-master.”

Token overhead is real. Even at the “core” tier, the server consumes ~5K tokens of context window just for tool descriptions. At the “all” tier, it’s ~21K tokens — a significant chunk of context that could otherwise hold code. Whether the task structure is worth that overhead depends on your project’s complexity.

Vertex AI provider gap. Issue #1648 reports that the Vertex provider can’t actually use Claude models because it routes through the wrong API endpoint. Enterprise users on Google Cloud infrastructure are stuck.

Security Posture

Telemetry is the headline concern. The Sentry integration capturing full AI prompts/responses by default (issue #1681) is the most immediate risk. Until this is addressed, anyone processing sensitive requirements or proprietary code should verify telemetry is disabled in their .taskmaster/config.json and confirm the opt-out actually took effect.

ToolTrust Grade B (scanned April 2026, 24 findings). The tool requests broad permissions (exec, filesystem, network access). Recommended to keep behind manual approval and not run unattended.

No CVEs assigned. No Task Master-specific CVEs in the NVD or GitHub Security Advisories as of this review.

tasks.json is local and unencrypted. Task data including AI-generated implementation details, PRD content, and complexity analysis sits in a plain JSON file. If your project directory is shared or version-controlled without .gitignore rules, this data is exposed.

Race condition data loss. The file-based storage without proper locking (issue #1567) means concurrent access can silently corrupt or lose task data. This is a reliability concern more than a security one, but in automated pipelines, lost tasks could mean skipped security-critical work.

How It Compares

vs. Roo Code Boomerang Tasks: Roo Code’s built-in task orchestration breaks projects into subtasks routed to specialized AI modes. It’s tighter integration (no MCP overhead) but locked to the Roo Code editor. Task Master works across Cursor, Claude Code, Windsurf, and others.

vs. manual TODO files / GitHub Issues: Task Master’s advantage is AI-powered dependency analysis and complexity scoring. The disadvantage is that tasks.json is an opaque format that doesn’t integrate with existing project management tools. Your tasks exist in Task Master’s world, not in Jira or Linear or GitHub.

vs. Claude Code’s built-in TodoWrite: Claude Code has a native task tracking tool, but it’s simpler — no dependency graphs, no PRD parsing, no complexity analysis. Task Master is the heavy-duty option for complex multi-phase projects.

vs. just using the AI agent directly: The fundamental question. If your project is straightforward, adding Task Master’s token overhead and learning curve may not be worth it. The sweet spot is complex projects with many interdependent tasks where the agent would otherwise lose track of what’s done and what’s next.

Who Should Use This

Task Master makes sense if you’re using AI coding agents for projects complex enough to need structured task management — think multi-week features with 20+ tasks and dependency chains. It’s most valuable in Cursor, where the integration is tightest, and for developers who already think in terms of PRDs and task breakdowns.

It does not make sense for quick scripts, small fixes, or projects where you can hold the full scope in your head. The context window overhead and setup time aren’t justified for tasks you could just describe in a single prompt.

Disable Sentry telemetry immediately after installation. Add "telemetry": { "enabled": false } to .taskmaster/config.json and verify it’s being read. Until issue #1681 is resolved, this is table stakes.

Bottom Line

Task Master is the most popular task management MCP server by a wide margin, and the core concept — structured, dependency-aware task planning for AI agents — is genuinely useful. The tiered tool loading and multi-model support show thoughtful design. But the default telemetry capturing full AI prompts, data integrity bugs in the task storage, and the Commons Clause license all warrant caution. It’s a powerful tool that’s growing faster than its rough edges are being sanded down.

Rating: 3.5 out of 5 — a strong concept with real utility, held back by privacy defaults and reliability concerns that matter most in exactly the production workflows where it’s most useful.

Review published April 20, 2026. This review is based on research of publicly available information including the GitHub repository, npm package, PulseMCP listing, release notes, issue tracker, and community discussion. We did not install or run this software.