"A system for task management in coding projects" matching MCP servers:

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

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

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

Enables external AI clients to invoke an autonomous coding agent that plans, fixes, and self-corrects code in an isolated Docker sandbox via run_task and run_tests tools.

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

Enables an Alexa+ host to place and manage customer-care calls by building a case, navigating phone menus, waiting on hold, explaining the issue, guarding sensitive disclosures, handing off identity checks, recording ticket numbers and promises, and scheduling follow-ups.

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.

Enables AI agents to call tools through a security gateway that intercepts requests, enforces deterministic policy and risk checks, blocks or escalates high-risk actions for human approval, and keeps tool credentials and audit logs protected.

Enables AI development tools to maintain context across chat sessions with automatic branching, progress tracking, and TODO management for different tasks.

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.

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.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables supervising and orchestrating local coding agents like Muse and AGY through a client-agnostic MCP interface, with isolated Git worktrees, safe execution, and deterministic verification.

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.

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.

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.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Governance framework for LLM agents. Exposes seven mechanically-enforced rules plus tools for preflight file protection, audit-chain verification, session handoff and worklog templates, MCP config auditing, and an emergency kill switch.

Enables agents to select the provably optimal set of candidate actions under a budget, with optional conflicts and prerequisites, and to re-plan to the single best next action after each outcome. Also validates any proposed selection for feasibility.

Enables AI assistants to manage tasks through YAML-based storage with subtask suggestions, status updates, and Mermaid Gantt chart generation. Supports hierarchical task structures with attributes like dependencies, milestones, and parallel execution.