"QA review sentinel test marker" matching MCP servers:

Enables Claude Code sessions to wait for a durable, exact-head GitHub pull-request review and continue the same review/fix/push loop without polling, using Supabase durable state and Realtime wake-ups via ordinary stdio MCP.

An autonomous MCP server for AI-powered software generation using Gemini and Groq, with features like dual-engine strategy, visual inspection, and QA sentinel.

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

AI code review against your team's own rules from your repository, before the pull request: two tools, review_changes and get_team_rules. Closed-source binary distributed via GitHub Releases; published in the official MCP Registry as dev.reviewgate/reviewgate.

Bridges multiple CLI coding agents (Codex, Cursor, OpenCode, Claude, Antigravity) into any MCP client, enabling delegation of prompts, parallel execution, and code review workflows.

A secure, containerized workspace for LLMs to autonomously code, test, and debug projects without risking the host machine.

An MCP server that lets Claude Code delegate coding tasks such as code generation, review, refactoring, and test writing to a local Ollama instance running a Qwen3 model.

Enables Codex to delegate review and implementation tasks to the local Qoder CN CLI as background jobs, with two permission tiers and polling-based status updates.

Enables Claude Code to delegate bounded repository tasks to DeepSeek as a local sub-agent, handling exploration, routine changes, and test runs within a controlled workspace and budget.

Enables MCP clients like Claude Code and Codex to delegate coding tasks to Cursor's CLI agent, which implements changes in the workspace and returns clean, structured results for review.

Enables AI coding agents to compile, test, run, hot-swap, and debug Java applications incrementally through MCP, providing runtime feedback for verifying code changes.

An MCP server that splits coding work between ChatGPT web (planning, review) and Codex CLI (implementation), enabling agentic workflows over a local repo with risk-gated execution and safety rails.

Autonomous coding pipeline exposed as an MCP server: plan, dispatch, review, and merge software stories through worktree-isolated agents. A frontier model (Claude) handles judgment — planning, review, risk adjudication — while a local model does the implementation, gated by TDD and a merge-time test rerun on the rebased branch.

A safe, local MCP server that lets Claude drive a controlled software-development loop (inspect, read, plan, patch, apply, check, analyze, fix, summarize) on a project, using deterministic tools and real diffs/test runs.

Exposes the local Claude Code CLI as a subagent for Codex, enabling background review and implementation jobs with two fixed permission tiers.

Enables MCP clients to control the local Codex CLI, including running and resuming agent sessions, forking, code review, applying diffs, and generating images, with background execution for long-running tasks.

MCP server that lets Claude Code ask GPT Codex for adversarial planning, code review, debugging, research, and risk triage without leaving your project workflow.

Enables structured multi-model AI planning sessions across multiple CLI coding tools, orchestrating independent planning, peer review, and final synthesis.

An MCP bridge that enables AI coding agents to delegate implementation tasks to the OpenAI Codex CLI, allowing the orchestrator to plan and review while Codex executes.

Bridges Claude Code and OpenAI Codex CLI for an interactive plan-execute-review workflow, enabling Claude to interview, design, and review while Codex implements code changes.