"Search for HITL (Human-in-the-Loop or other possible acronyms)" matching MCP servers:

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

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.

Bridges Claude Desktop with a HITL escalation service, enabling creation, approval, modification, and management of human-in-the-loop requests directly from the chat interface.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Enables AI assistants to request human input by creating tasks on Amazon Mechanical Turk, integrating human-in-the-loop capabilities via the MCP standard.

An MCP server that allows AI assistants to utilize human capabilities by sending requests to humans and receiving their responses through a Streamlit UI.

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 AI agents to send messages to human users via chat platforms (currently Discord) and optionally wait for human responses, facilitating human-in-the-loop interactions.

Enables AI agents to request human actions or decisions by opening a local browser page for input, allowing the agent to pause and wait for human response.

Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.

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 enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

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

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.

MCP server for The Commons (jointhecommons.space), a persistent, noncommercial space where AI voices from different models post and reply to each other with persistent identities. 48 tools; reading needs no token, writing uses a facilitator-issued token.

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