"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 agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.

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.

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

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.

Enables autonomous agents and MCP-compatible clients to enforce policy-approved actions using risk tiers, reversible checkpoints, and token approval bounds.

Integrates Loopy's AI agent loop library with MCP-compatible clients like LM Studio, enabling discovery, analysis, crafting, and execution of bounded agent workflows.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

MCP server for AgentDNS that allows Claude or any MCP client to register a persona, discover and call other agents, and handle incoming messages with human-in-the-loop approval.

Enables delegating tasks to a separate, full agentic loop running on a different model, with its own tool access, returning only the final result.

An MCP server that enables human-in-the-loop elicitation, letting AI agents ask users questions via tools like elicit_confirm and elicit_form, with diagnostics to verify host elicitation support.

Human-in-the-loop approval inbox for AI agents: an agent proposes an action (send email, post comment, run a command), a human approves, rejects, or edits it from a web, mobile, or Slack/Discord/Telegram inbox, and the agent only runs on approval. Full audit trail, self-hostable (MIT).

Enables AI coding agents to request human-in-the-loop approvals, choices, refinements, and notifications via MCP, with fail-closed semantics and no remote approval surface.

Enables MCP agents to submit content for human review via LoopQuest, returning verdicts asynchronously.

Allows two AI coding agents on different machines to securely pair and share files, context, and conventions through an end-to-end encrypted peer-to-peer channel with human-in-the-loop consent.

Enables any MCP client to drive a multi-agent orchestration engine with planning, specialist tools, critic revision, and human-in-the-loop approval for sensitive actions.

Human-in-the-loop approval gate for AI agents. Your agent calls submit_approval before any irreversible action; a human reviews on a branded page; a signed webhook fires back with the decision.

Enables coding agents to run persistent, budgeted work sessions with dependency graphs, human decision queues, required validation, and clean handoffs for autonomous or human-in-the-loop execution.

Human-in-the-loop automation framework. Log them in, watch them run.

Human-in-the-Loop approval gate for AI agents to assess risky operations and require user approval via Cursor forms.