"Loop" matching MCP servers:

DingDawg Loop Protocol (DDLP) — safe scheduled AI agents with governance gates. Every loop execution is verified, receipted, and fail-closed. MCP-native, works with CrewAI, LangGraph, Claude Code, Cursor.

Enables an autonomous build loop where Jev makes typed routing and completion decisions while Claude Code writes code, runs checks, and requires independent review before declaring tasks done.

Enables AI agents to autonomously develop and test Godot 4 games through an MCP-based feedback loop, providing tools for authoring, running, observing, playtesting, and verifying game projects.

Enables hands-free voice conversations with Claude using real-time speech recognition and text-to-speech on macOS. Creates a self-sustaining conversation loop where Claude can autonomously listen, respond, and continue the interaction without keyboard input.

Enables context-loop navigation in agents by exposing goto and loop_start_marker tools that delegate to a host over a Unix socket, allowing bookmarking and jumping between context iterations.

An MCP server that automates the full software development lifecycle through an AI-driven TDD state machine. It handles everything from task decomposition and test-driven development to integration testing and automated pull request creation.

The industry-standard persistent memory and state manager for long-running agentic workflows.

Enables AI agents to orchestrate tasks as a DAG with automated validation loops, executing validation commands, tracking statuses, and allowing iterative code fixes until tasks pass.

Enables LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

A referee for self-improving AI agent loops that mines past sessions, improves workflows, and never stops until told.

A referee for self-improving AI agent loops that mines past sessions for working workflows, improves them, and never stops until you stop it.

MCP server that inspects older repos, identifies stale guidance, and installs a managed foundation for better Copilot work including instructions, objectives, specialist agents, and insights.

A referee for self-improving AI agent loops that mines past sessions for effective workflows, improves them, and requires measured proof before declaring anything better or done, never stopping until the user stops it.

Enables autonomous data quality inspection and repair workflows. It scans DuckDB warehouses for anomalies, generates and verifies fixes in a dry-run copy, then applies them after validation, with full audit logging.

Provides coding agents with 63+ bounded, verifiable work loops, enabling goal-driven loop selection and executable protocol generation via MCP tools.

An intelligent middleware that determines when human intervention is necessary in AI agent operations using a sequential scoring system that evaluates multiple dimensions of a request.

Autonomous, carbon-aware building management system that pairs EnergyPlus digital twins with LLMs via the Model Context Protocol (MCP) for dynamic HVAC optimization and grid carbon reduction.

A goal-agnostic parallel orchestration framework that enables sophisticated multi-agent coordination for tasks like code generation, UI development, and research through specification-driven architecture. It utilizes wave-based generation and intelligent context management to execute complex, iterative agentic loops.

An AI debugging agent MCP server that enables autonomous plan-act-observe debugging workflows, allowing repository exploration, code inspection, human-approved edits, and test execution through structured MCP tools.

Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.