"Overview and Applications of Mixture of Experts in Machine Learning" matching MCP servers:

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 manager MCP server that demonstrates core MCP concepts (resources, tools, prompts, stdio) and syncs data with a Next.js web app.

Enables AI-powered task management in OmniFocus with support for project reviews, planned dates, repeating tasks, custom perspectives, hierarchical subtasks, and advanced filtering. Perfect for Claude AI integration with comprehensive CRUD operations for tasks, projects, and folders.

An MCP server for managing tickets and meetings with CRUD operations, backed by PostgreSQL, and includes a REST API and React frontend.

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

An MCP server that enables AI assistants to interact with Atlassian Jira and Confluence across Cloud and Server/Data Center environments. It supports tasks like searching and summarizing documentation, managing Jira issues, and creating content through natural language.

Enables AI to read live weather data and manage a persistent to-do list through MCP tools, demonstrating both read and write capabilities without API keys.

Goodday‑MCP is a lightweight Model Context Protocol (MCP) server designed to seamlessly integrate with the Goodday project management platform via its API v2. It enables querying of projects, tasks, and users—without altering any data—making it ideal for secure context-aware applications

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

A collection of MCP servers for Slack, Jira, Confluence, and release coordination to automate QA workflows and daily release status reporting.

Enables comprehensive management of OpenProject work packages, projects, comments, and relations through natural language. Supports creating, updating, and organizing tasks with assignees, watchers, hierarchies, and inter-task relationships.

A Model Context Protocol (MCP) server for Hubstaff API integration, enabling seamless time tracking, project management, and team collaboration through AI assistants.

Provides AI clients with per-project todo management and persistent context notes via MCP, enabling task tracking and resume summaries.

Enables a weekly review of Todoist tasks by listing overdue tasks with key signals (days overdue, priority, times postponed) and letting Claude propose fixes per task, which you approve or veto in chat before any changes are applied.

Read visual bug reports from Feedbug in your AI coding agent: screenshot, console logs, failed network requests, session replay and the DOM context of the element the tester clicked. Trace the bug to its source file, comment and resolve without leaving the editor.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

Enables AI assistants to search live jobs, view full details, save and track applications in a Kanban board, set follow-up reminders, and subscribe to job alerts—no account needed for searching.

Routes tasks from AI agents to human workers via webhook providers with smart matching, fallback chains, and proof-of-completion tracking.