"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 Codex and DeepSeek Harness to maintain a local, traceable record of current task intent, separating requirements, evidence, unknowns, and disagreements while preserving corrections without erasing history.

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 agents to generate TTS audio, verify it with independent ASR, measure quality against gates, escalate retries, and receive structured QC reports to improve text.

Lets AI agents retrieve, search, and inspect generated lesson packs and rubrics, and enqueue new concepts for the lesson production workflow.

A cross-platform MCP server implementing the Ralph Loop iterative development technique where a worker model does the work and a reviewer model provides cross-model review until approval.

Enables AI agents to index, search, and validate lessons learned across project repositories via a local SQLite index, providing learning-loop resources and verified-solution tracking without loading full files into conversations.

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.

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.

Automates a three-phase development workflow: planning, spec generation, and iterative task execution with verification.

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.

MCP server that provides tools to add to, search, and manage a wiki knowledge base, enabling AI chat tools to contribute and retrieve information from the wiki via natural language prompts.

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.

Enables Claude Code sessions to wait for a durable, exact-head GitHub pull-request review and continue the same review/fix/push loop without polling, using Supabase durable state and Realtime wake-ups via ordinary stdio MCP.