"Loop" matching MCP servers:

Enables AI-moderated interviews that become cited, searchable transcripts, with a repository Q&A tool that returns exact transcript quotes.

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.

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.

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.

Automates a three-phase development workflow: planning, spec generation, and iterative task execution with verification.

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

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

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 AI assistants to create, manage, and discover family gift registries. Users can add links from Amazon, Target, and more through natural language.

Provides bounded MCP tools for AI agents to inspect, write, verify, and diff files in a sandboxed repository, requiring human approval and logging an audit trail.

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.

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.

Runtime governance and budget guardrails for Claude Code, Cursor, and autonomous AI agents. Enforces per-session spend caps, verifier safety gates, and runaway loop prevention.

LoopSense is an open-source MCP server that closes the feedback loop for AI coding agents — giving them real-time visibility into CI results, deployments, test outcomes, and file system changes.

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

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.

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.

Demonstrates agent-native platform onboarding with human-in-the-loop API key provisioning, plus live usage and rate-limit queries via Anthropic Admin APIs.

A generic OpenAI-compatible subagent MCP server that lets Claude Code delegate coding grunt-work to a local model via a tool-calling agent loop, with configurable concurrency and backend.