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

feedback-loop-mcp

Persistent memory and pattern learning for AI coding assistants via MCP.

Enables AI clients to write, run, and visually render code in a single loop, combining secure execution via Judge0/Codebox with Playwright screenshots.

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 AI agents to directly manipulate Jupyter Notebook cells and structure through a secure, well-defined API, overcoming Cursor's AI agent mode limitations.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

A Python MCP server that allows programmatic interaction to find torrents programmatically on ThePirateBay, Nyaa and YggTorrent.

Enables integration of Perplexity's AI API with LLMs, delivering advanced chat completion by utilizing specialized prompt templates for tasks like technical documentation, code review, and API documentation.

A lightweight and fast MCP server that enables AI agents to efficiently discover and execute tools through progressive disclosure, minimizing context consumption while supporting safe code execution in external environments.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

A Python MCP server that allows programmatic interaction to find torrents programmatically on YggTorrent and La Cale.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.

This MCP server integrates AI assistants with Unity Editor, allowing them to create scenes, generate scripts, simulate input, and automate workflows using 91 built-in tools.

Enables AI assistants to build and manage Unity projects directly through the Unity Editor, providing 150+ tools for scene creation, script generation, play mode control, input simulation, and performance analysis.

Enables AI assistants to control and automate the Unity Editor, allowing them to build games and scenes from natural language commands.

Enables AI assistants to operate directly inside a running Unity Editor project, using built-in tools for scene creation, script generation, play mode automation, input simulation, and performance analysis.