"Progress" matching MCP servers:

Self-hosted MCP + REST continuity hub for coding agents. Tracks unfinished work, progress, overlap, reminders, and project context across Cursor, Codex, Claude Code, and other MCP clients.

5-voter Byzantine council halts agentic loops when no real progress is happening, preventing token waste on infinite spins.

Enables AI agents to track, search, and retrieve their progress across projects with persistent memory using SQLite storage and LLM-powered summarization. Supports logging completed work, searching previous entries, and retrieving context for multi-step or multi-agent workflows.

Enables AI agents to manage tutoring workflows by listing students, tracking progress, logging session notes, assigning homework, and generating parent-friendly progress reports through natural language.

MCP server implementing progressive disclosure for skill instructions, loading skills on-demand for token efficiency.

Enables running example MCP servers over both stdio and streamable HTTP transports, providing tools for integer addition, streaming progress counts, and greetings, and can be installed directly from GitHub via uvx.

Provides structured spec-driven development workflow tools for AI-assisted software development with sequential spec creation (Requirements → Design → Tasks). Features a real-time web dashboard for monitoring project progress and managing development workflows.

Enables MCP clients to access WeChat Reading data and actions, including book search, book details, reading progress, highlights, reviews, and reading statistics, via a URL-based SSE or Streamable HTTP server.

Enables agents to monitor token/usage limits and persist work checkpoints, allowing them to pause, resume, and track progress across sessions.

Enables AI assistants to manage CData Sync operations, including data synchronization jobs, connections, and ETL processes through stdio or HTTP transports. It provides tools for executing jobs, monitoring real-time progress via Server-Sent Events, and handling comprehensive workspace configurations.

A secure Model Context Protocol server for interacting with the WorkBoard OKR and strategy execution platform. It enables users to manage user profiles and track goal progress through a standardized, containerized interface.

A deep-research MCP server exposing a single task-augmented tool that runs GPT-Researcher with Tavily search, Anthropic Claude, and OpenAI embeddings, streaming progress via MCP tasks and Upjack entity stream.

Enables monitoring and analysis of Ansible playbook executions through the Ara Records API. Provides real-time playbook monitoring, task progress tracking, and comprehensive access to Ansible execution data.

Enables users to search for courses, view detailed syllabus information, and manage enrollments and progress on Coursera. It supports automated interactions including viewing certificates through local or cloud-based browser environments using Playwright.

Bridges MCP clients such as Claude Code and Cursor to independent Codex app-server sessions, letting them start, poll, steer, review, compact, cancel, and answer prompts for asynchronous Codex tasks over local stdio or a remote authenticated WebSocket. Runs read-only by default, with workspace-write sandboxing only when a user explicitly enables it, and reports job status, model listing, and progress back to the client.

This MCP server connects Motimate with Claude, enabling direct access to training content, learning paths, and progress data via chat.

Enables Claude and ChatGPT to share a single study-progress database for interview prep, coursework, and spaced repetition, with per-user data isolation and server-side SM-2 scheduling for due reviews and study plans.

MCP server that exposes an Italian textbook's OCR content, structured exercises, and progress tracking, enabling chatbot agents to interact with the book for language learning.

A lightweight short-term memory MCP server that automatically stores and recalls working context, session state, and task progress for AI agents. Memories auto-expire after 24 hours and integrate seamlessly with workspace-aware storage across multiple projects.

Enables MCP clients and LLMs to create, edit, list, and delete arbitrary daily numeric goals, log and correct entries, and retrieve timezone-correct progress, history, and streak status with at-least or at-most targets.