"Loop" matching MCP servers:

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

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 an autonomous build loop where Jev makes typed routing and completion decisions while Claude Code writes code, runs checks, and requires independent review before declaring tasks done.

Local production engineering platform that indexes Python codebases and exposes semantic code analysis, git risk scoring, and log correlation through MCP tools for AI assistants.

Enables Codex and DeepSeek Harness to maintain a local, traceable record of current task intent, separating requirements, evidence, unknowns, and disagreements while preserving corrections without erasing history.

An MCP server that performs independent verification of artifacts against criteria using a different AI model lineage (codex, OpenAI, or Gemini) to catch defects that same-family checks might miss.

Enables agents to generate TTS audio, verify it with independent ASR, measure quality against gates, escalate retries, and receive structured QC reports to improve text.

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

Lets AI agents retrieve, search, and inspect generated lesson packs and rubrics, and enqueue new concepts for the lesson production workflow.

feedback-loop-mcp

Enables users to view and edit Microsoft Loop pages as interactive markdown widgets directly within the chat interface. It supports inline rendering and provides tools for requesting page updates through a host's Loop MCP server.

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.

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.

A cross-platform MCP server implementing the Ralph Loop iterative development technique where a worker model does the work and a reviewer model provides cross-model review until approval.

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 LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

A loop-aware MCP server for your own ComfyUI that enforces a build-run-look-critique-fix cycle, making the model iterate on pixel quality until the output matches the brief.

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