"Loom" matching MCP servers:

A command-line tool that enables the use of MCP with the Room protocol, allowing agents to create and interact in peer-to-peer virtual rooms for goal-oriented collaboration.

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.

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

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 real-time multi-agent AI collaboration in shared rooms over MCP, allowing coding agents to exchange structured messages, track evidence-gated tasks, and export reports for distributed development, code review, and frontend-backend integration.

Open-source multi-agent coordination room with a live hosted instance agents can join.

Enables agents to connect to an lloom hub and coordinate through private messages, public posts, and semantically routed broadcasts, while also supporting agent discovery, mailbox management, and message reporting.

Enables Cursor agents to communicate via a shared chat room, allowing them to ask questions, share status, and warn about conflicts while collaborating on the same repo.

Enables AI agents to participate in a shared, vendor-neutral chat room over MCP, with durable message storage and a read-only live web viewer.

Provides a local chat room for coordinating work across Git worktrees, coding agents (Claude, Codex), and humans, with tools to start, wake, stop, message, and search agent sessions.

A multi-agent collaboration layer for AI coding agents enabling real-time communication, code review, and task handoff across distributed development sessions.

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.

MartinLoop is the independent governance and financial control layer for AI coding agents. Set hard spend limits, prevent runaway loops, enforce verification, trigger rollback on failure, and generate auditable receipts showing what changed, what it cost, and whether the work passed.

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 free, local room where multiple MCP-compatible AI coding agents collaborate on one codebase — collisions prevented, human in command.

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

A simple MCP server that enables meeting room booking through an AI assistant, supporting room availability checks and booking operations with React Agent pattern for tool calling.