"Progress" matching MCP servers:

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

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 interaction with ESP32/MicroPython boards via tools for flashing firmware, running REPL code, file management, and serial capture, with support for progress notifications, cancellation, and error handling.

An MCP server that integrates Codex CLI into Claude Code workflows for code writing, execution, and review with session management. It features real-time progress monitoring via a local HTTP dashboard and supports detailed configuration for various coding tools.

Enables running Jupyter notebooks on temporary AWS compute (EC2 + SSM) with cost estimation, dry-run planning, progress reporting, and automatic cleanup.

A Model Context Protocol server that enables AI assistants to build and test Xcode projects directly through a standardized interface, with capabilities for running tests, monitoring progress, and accessing logs in real-time.

An MCP server that implements iterative refinement of responses through self-critique cycles, breaking the process into discrete steps to avoid timeouts and show progress.

Enables users to run and manage BBOT security scans through the MCP interface. Provides comprehensive tools for executing reconnaissance scans, monitoring progress, and retrieving results with support for concurrent scanning operations.

Enables MCP-compatible AI clients to invoke CLI-driven agent tools over Streamable HTTP, including shell execution, file operations, patching, image viewing, web search, and nested agent tasks, with permission modes and real-time progress streaming.

exposes a remote MCP endpoint so agents can: run strategy backtests by symbol/timeframe/date range, pass strategy inputs programmatically, receive structured backtest results (trades, win rate, profit, drawdown), keep long-running runs observable via progress notifications, support Binance Futures tickers only, enforce a maximum of 1440 candles per backtest, apply a rate limit of 3 backtests per

Enables LLMs to manage and run machine learning training jobs on a remote server, including syncing code, submitting experiments, monitoring progress, reading TensorBoard metrics, and receiving completion notifications.