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

Navigator for BPC Agentic Procurement, providing tools for submitting RFQs, checking status, requesting terms, and escalating to a human.

Enables AI assistants to interact with humans through GUI dialogs for text input, choices, confirmations, and information display.

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

Enables authorized web/API security analysis as a stateful, multi-stage workflow with persistent session state, human-controlled validation, candidate versus confirmed finding tracking, and stop conditions.

Enables AI agents to ask human operators questions through polished browser dialogs, supporting text/choice/confirmation inputs and file attachments.

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

Enables autonomous agents and multi-agent frameworks to route actions through deterministic human approval gates before execution. Enforces risk tiers, reversible checkpoints, and token approval bounds so that risky or costly operations can be reviewed, bounded, and rolled back without LLM round-trips.

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.

Enables any MCP-capable harness to run queries through a real browser driving real search engines in a configurable fallback chain (Google, DuckDuckGo, Bing, Baidu, Sogou), with CAPTCHA or sign-in handoff, persistent per-engine cookies, and reported provenance for each result.

Enables AI agents to request human decisions and send notifications through chat platforms like Slack and WhatsApp, with ephemeral in-memory pending requests and no central backend.

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.

MCP server that lets an AI analyst place real bets on Binance prediction markets, using Kelly criterion sizing, and tracks its performance through a transparent record of betting slips and outcomes.

Enforces mandatory human approval before LLM agents can edit files, create files, or execute shell commands, using session tokens and audit logging.

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.

Real human judgment as agent tools -- an AI agent can ask a question and get back a structured, schema-validated JSON answer from a real quality-scored human. 16 response types (yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, voice/video/photo capture). Fully programmatic signup with a $5 free trial credit, no card required.

Connects MCP clients to a local social engine that reads bounded posts, comments, replies, and feeds into local inboxes, turns source material into drafts and briefs, and publishes exact frozen jobs with recorded provider IDs and receipts across Instagram, Threads, and Substack.

Enables AI agents to pay for MCP tool calls using either Bitcoin/Lightning (L402) or USDC (x402) rails, with unified settlement and a 21% discount for Bitcoin payments.

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

MCP server enabling agents to interact with a human-in-the-loop B2B service in Vienna, supporting quote requests, task tracking, messages, and cancellation via bearer-token authenticated tools.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

Enables LLMs to send DM messages to a human via Discord webhook when they need attention — a question, blocker, decision, or completed work — using a standardized, user-controlled format read from a local file or hosted URL.

MCP server that analyzes your writing style to generate personal voice rules, scores drafts for AI slop, and verifies authorship, enabling LLMs to write in your specific voice.

An MCP server that lets users find nearby shops and POIs around a location and find targets such as gas stations or charging points within a given radius along a driving route, using the Amap (Gaode) Web Service API with built-in API key entry and validation.

Enables reading, listing, creating, and editing Microsoft Loop pages as Markdown through browser automation.

Enables coding agents to see the UI they just changed without flooding their context window, returning a structured text summary (console errors, failed requests, overflow, layout shifts, accessibility violations) plus small crops of only the regions that changed since the last capture. All output is fitted into a chosen token budget, with captures stored on disk and evicted automatically.

MCP server for creating and managing digital human models with built-in EU AI Act compliance, part of the MEOK AI Labs ecosystem.

Enables a coding agent to keep one JSON feature specification per feature, governed by a progressive schema, and to render it as editable Markdown documents for a product owner. Answers and direct Markdown edits are reconciled back into the validated model through stdio MCP tools, applying coherent changes and reporting those that widen scope or contradict the model.

Provides a stable, side-effect-free MCP tool contract that enables AI workflows to be prototyped, demoed, tested, or dry-run without connecting to databases, APIs, files, or other real data sources. It enables swapping in a real data server later without changing the AI-facing interface.

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.