"Task tracking tools for enabling LLMs to accomplish complex tasks" matching MCP servers:

MCP server for agentic task orchestration and management, enabling AI agents to create goals, plan tasks with acceptance criteria, track dependencies, request human input, and provide proof of completion.

Task manager for AI agents with projects, priorities, and status tracking. Reads are free, writes require Veyra commit mode.

An MCP server for Task Trellis that provides tools for AI coding agents to manage tasks, currently featuring a simple hello_world demonstration tool.

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

This MCP server enables agents to manage complex tasks by providing tools for registration, complexity assessment, breakdown into subtasks, and status tracking throughout the task lifecycle.

A campaign and task management MCP server for AI coding assistants, enabling dependency tracking, acceptance criteria, testing strategies, and progress monitoring for projects.

Provides a task passport system for handoff across AI harnesses, enabling users to list, open, create, and checkpoint tasks with stable IDs and verified state.

Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.

Enables reviewing and dispatching existing Codex tasks across projects through a lightweight control panel, including scanning task inventories, confirming Continue or Optimize prompts, and tracking dispatch progress.

Enables AI agents to autonomously manage and improve execution processes for repetitive task types by storing reusable task contexts with associated artifacts (practices, rules, prompts, learnings) and providing full-text search across historical best practices.

Enables long-running coding or documentation tasks to save incremental checkpoints, resume after interruptions, restore any previous step, and hand off work across sessions or machines using only the Python standard library.

MCP Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

A task management system for LLM-based agents that stores all tasks in a single tree, available immediately after the server starts.

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

Open Task Relay provides free, bounded public-good tasks that autonomous AI agents can discover, complete, submit, and independently verify. Its public TypeScript source runs locally with a Cloudflare Workers-compatible runtime and SQLite/D1, exposing MCP over HTTP.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

Enables Codex to spawn, monitor, send messages to, and list DSH background tasks via a local REST bridge, wrapping six task-coordination endpoints as MCP tools.

A Model Context Protocol server providing comprehensive task management capabilities with support for project organization, task tracking, and automatic PRD parsing into actionable items.

Enables AI coding agents from different MCP-capable clients to exchange messages in real time through a shared in-memory broker. Supports agent registration, inboxes, and live event streaming without requiring a database.

An intelligent task management system that provides structured workflows for AI Agents to plan, decompose, and execute complex programming tasks. It features a dedicated research mode for technical investigations and a task memory function to optimize workflows and avoid redundant coding work.