Part of our Developer Tools MCP category.
At a glance: 27,200 GitHub stars, 2,500 forks, 1,216 commits, last commit April 23 2026, npm v0.43.1 (no release in 49 days), 36 tools (in three loading tiers), MIT license with Commons Clause, ~30,500 npm downloads/week, PulseMCP ~1.8M all-time visitors (#38 globally, ~31,100 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 metadatanext_task— get the next actionable task based on dependencies and statusget_task— get details for a specific taskset_task_status— update task status (pending, in-progress, done, etc.)update_subtask— update subtask details and statusparse_prd— parse a PRD document into structured tasks with dependenciesexpand_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:
- Initialize a project with
initialize_project - Parse a PRD — feed it a requirements document, get back a structured task list with numbered tasks, dependencies, priorities, and complexity scores
- Analyze complexity — get a report on which tasks are hardest, where risks cluster
- Work through tasks — use
next_taskto get the next unblocked task, implement it, mark it done - Expand tasks — break complex tasks into subtasks as you go
- 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. 1,216 commits and responsive issue triage with priority labels — the project isn’t abandonware. Notably, the race-condition data loss bug (issue #1567) was fixed in v0.42.0: proper file locking via proper-lockfile, atomic writes, and stale lock cleanup closed that gap.
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 as of May 19) 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. Your PRDs, task descriptions, implementation details, and AI-generated code are sent to Sentry by default. PR #1696 (“fix: disable AI content recording and PII collection in Sentry by default”) exists but remains unmerged after 49 days. The opt-out mechanism is still fragile — if .taskmaster/config.json isn’t found in the working directory at MCP server start time, telemetry defaults to fully enabled.
152 open issues with unresolved data integrity bugs. Issue #1683 reports task status drift — set_task_status returns success without updating the on-disk file, and next_task may pull from the wrong tag context. Issue #1647 reports task ID collisions when moving tasks between tags (PR #1665 exists but unmerged). For a task management tool, silent state corruption is an existential problem.
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 npm release in 7 weeks. v0.43.1 (March 31, 2026) remains the latest published version as of May 19. The repo has seen activity (documentation link updates, April 23) but zero code releases in 49 days — leaving the unfixed telemetry issue and open data integrity bugs sitting in a gap between commits and shipping.
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 — now fixed. Issue #1567 (multiple Claude Code windows corrupting tasks.json) was resolved in v0.42.0 via proper-lockfile and atomic writes. This was a meaningful fix for multi-agent workflows.
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 originally published April 20, 2026; refreshed May 19, 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.