"Replicating ChatGPT Memory Functionality for Task Management and Skill Tracking" matching MCP servers:

Enables natural language time tracking through Claude with markdown file storage. Supports multi-company tracking, flexible time parsing, auto-calculated summaries, and commitment warnings all through conversational input.

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 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.

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.

Provides access to OpenAI's ChatGPT API with web search capabilities for Claude and other MCP clients. Supports various GPT models with configurable parameters like reasoning effort, temperature, and streaming mode.

An MCP server for task orchestration.

Enables AI assistants to manage tasks through a comprehensive interface with 8 tools for creating, updating, searching, and tracking tasks with priorities, categories, and due dates. Features persistent file-based storage, advanced filtering, and task statistics for complete task management workflow.

A Model Context Protocol server that allows AI assistants to communicate with the ChatGPT desktop app on macOS, enabling users to send prompts to ChatGPT from any MCP-compatible assistant.

A Model Context Protocol implementation that provides a standardized interface for task management, supporting both STDIO mode for CLI/AI applications and HTTP+SSE mode for browser-based clients.

Enables AI assistants to manage tasks, projects, and dependencies through MCP protocol with embedded Web UI, multi-workspace support, and Turkish language capabilities for productivity tracking.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

Enables creation and management of scheduled tasks with interval, cron, or one-time triggers. Persists tasks in SQLite and supports MCP sampling to automatically invoke AI agents when schedules trigger.

Enables agents to persist and resume long-horizon tasks using cryptographic event-sourced checkpoints, idempotency deduplication, scheduled heartbeats, pause/resume tokens, and fail-safe recovery.

A sophisticated task orchestration system with role-based workflow management and Trello integration.

Enables ChatGPT to perform filesystem operations, execute PowerShell commands, and manage n8n workflows on a Windows machine.

A local task management MCP server that enables users to create, update, and manage tasks through natural language conversations with Claude. It provides nine tools for comprehensive task management including creation, filtering, searching, and daily planning without requiring a separate UI or backend service.

Enables asynchronous task execution and scheduling using RabbitMQ, Celery, and Redis/SQLite. Supports immediate background task execution, scheduled tasks with ISO 8601 timestamps, task status monitoring, cancellation, and completion notifications.

Enables ChatGPT to connect to WhatsApp via a self-hosted bridge, allowing users to list conversations, search contacts, read messages, and generate automated daily reports for agencies.

Connects AI assistants to your n8n instance to manage workflows, executions, credentials, and user settings through 31 specialized tools. It enables users to perform n8n operations like workflow creation, activation, and execution history retrieval via natural language.

Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools It can solve problems like: You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't: Read PDFs/DOCX — The AI can't open binary files Extract structured data — Copy-pasting loses formatting, metrics, and context Compare at scale — No consistent scoring across candida