"Progress" matching MCP servers:

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.

MCP server that bridges Claude with KiloCode, enabling Claude to act as architect and delegate coding tasks to Kilo's fast CLI agent while monitoring progress and reviewing results.

An experimental MCP server that provides web search capabilities using the Tavily API. Still in progress, not production-ready.

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.

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 users to create and refine a research outline, queue report generation in the background, and retrieve progress or completed Markdown reports, with automatic checkpointing so unfinished work can resume after a restart.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.

Read-only MCP server that lets AI agents query SageRead data: books, reading progress, stats, notes, AI chats, papers, and semantic vector search, with API keys staying inside the app. Supports both Better SageRead and upstream SageRead data directories.

Routes each coding task to the best-fitting spec-driven-development framework with explainable rules, then enforces deterministic lifecycle gate checks between phases so progress can't skip required artifacts or evidence. Assembles per-phase context packs from a company knowledge base with app-scoped memory and cross-app lookup, while the host agent remains the only actor that edits files or runs tests.

Connects AI assistants to the Maestro research framework to orchestrate multi-agent research missions, including planning, research, and writing phases. It enables users to launch research tasks, track real-time progress, and retrieve comprehensive structured reports and notes.

A multi-agent AI tutor that delivers personalized lessons, resolves doubts with RAG, generates quizzes, and tracks progress, all accessible via MCP for Claude Desktop.

Tracks learning progress and generates personalized daily learning insights using RAG to fetch relevant content from blogs, RSS feeds, and Reddit based on your current topics and learning goals.