"Automating Task Assignment Based on Priority with Reshuffling Capabilities" matching MCP servers:

Enables MCP-based integration with the Priority ERP OData REST API, with strict environment validation and a read-only default. As of the current README, no tools are registered yet; the server only boots and waits on stdio.

Elevate your LLM task management with Task Orchestrator, an MCP server that empowers you to define, organize, and track goals and tasks with hierarchical precision. Integrate intelligent task management into your workflow.

Enables AI assistants to manage GitHub issues using deterministic priority scoring and concurrency-safe locking. It supports a structured eight-phase workflow from issue selection through pull request review to ensure consistent prioritization and progress tracking.

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 server for task orchestration.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Enables users to manage tasks through a simple JSON file interface. Provides basic task management functionality by reading and writing to a configurable tasks.json file.

A Model Context Protocol server for intelligent task management in AI-powered development environments, providing file-based storage, dependency management with cycle detection, and an interactive CLI.

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 assistants to manage Dida365 (滴答清单) tasks through the Model Context Protocol. It supports full task lifecycle management including creating, updating, and filtering tasks with automatic timezone handling.

Middleware that converts a serverless handler with OpenAPI spec into an MCP endpoint following stateless MCP principles.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

A gamified task management MCP server that mimics League of Legends ranked match system, enabling users to add tasks, complete them to spin a gacha, and earn or lose LP to change ranks.

MCP server deployed on EdgeOne Pages, providing a Streamable HTTP MCP server for intelligent chat applications with tool capabilities like generating webpages.

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

Enables Claude to interact with System Task projects, teams, and tasks, providing daily briefs, project reports, team load, and risk identification, as well as creating and updating tasks and demands.

Provides task and project management tools for AI agents, including todo list management, prioritization frameworks like Eisenhower Matrix and RICE scoring, time estimation using PERT, daily standup generation, and sprint burndown calculation.

Enables agents to discover and retrieve contract details for Task Relay F1-F5 paid x402 machine jobs, but does not execute work or handle payments.

MCP server for Apple Notes enabling read, search, write, and sync of notes with high fidelity, including styles, attachments, and bidirectional Markdown sync.

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.