현재 릴리스: v3.22.2 — clone main or download the release archive. npm 패키지가 없습니다. 소스에서 빌드 (npm run build).

고정 7단계를 통해 AI 코딩 에이전트 작업을 하는 MCP 서버 코드로 바로 점프 대신 사이클. 커서에 내장되었지만, stdio에 일반 MCP를 호출하고 지원하는 모든 클라이언트와 함께 작동해야합니다 도구.

이 저장소는snapshot, 라이브 트리입니다. M7을 개발 6 월 2025 및 중반-2026 나 및 내 팀에 의해 사용되는 개인 도구. 이 버전 여기에 게시 된 것은 7 월 2026의 repo의 상태입니다. 그 닫은 배포 (self-update client, 라이센스 체크, 개인 액세스 문)와 제거되는 1개의 unfinished 연구 단위. 개발은 개인적으로 계속; 나는 또는 더 동기화 할 수 없습니다 나중에. 변경된 경우 CHANGELOG.md 를 참조하세요. docs/WHY OPEN SOURCE.md는 왜 이렇게 간행되는지 특허 또는 회사로 추구하는 오픈소스로서

무엇 그것

핵심 아이디어는 간단합니다: 대리인 접촉 부호의 앞에 그것을 통해서 갈 필요가 있습니다 발견, 아키텍처, 솔루션, 영향 분석 및 구현

M7 MCP

Current release: v3.22.2 — clone main or download the release archive. There is no npm package; build from source (npm run build).

An MCP server that makes an AI coding agent work through a fixed seven-phase cycle instead of jumping straight to code. It was built for Cursor, but it speaks plain MCP over stdio and should work with any client that supports tools.

This repository is a snapshot, not the live tree. I developed M7 between June 2025 and mid-2026 as a private tool used by me and my team. The version published here is the state of the repo as of July 2026 with the parts tied to that closed distribution (self-update client, license check, private-access gate) and one unfinished research module removed. Development continues privately; I may or may not sync more of it later. See CHANGELOG.md for what changed when, and docs/WHY_OPEN_SOURCE.md for why this is published as open source rather than pursued as a patent or a company.

What it does

The core idea is simple: before the agent touches code it has to go through discovery, architecture, solution, impact analysis and an implementation simulation, write an artifact for each, and pass a reflection gate between phases. The server does not trust the model to “remember” the rules: it issues a gate ID with every phase instruction, rejects reflections that do not quote it, re-injects a compact rule summary after N calls to mcp_m7_cycle within the same phase (the MCP server only observes its own tools, not the client’s other actions), and keeps the cycle state on disk so a new chat can pick up where the old one stopped.

Around that cycle there is a set of supporting tools:

• Memory with tiers (L1/L2/L3), confidence decay, promotion and a “fog of war” that tracks what is known, assumed, stale or unknown about the project. Optional ChromaDB backend for semantic search; falls back to local JSON.
• Task analysis and routing (mcp_m7_analyze_task): classifies a request, creates a session, flags scope drift, decides between a short path and the full cycle.
• Hive: run several model instances (OpenAI / Anthropic) on a question and merge the answers. Needs API keys.
• TSI (tech safety intelligence): YAML pattern packs (security, performance, architecture, testing, deployment) used to warn about known footguns before implementation.
• Task graph, task transfer between chats, architecture advisor, knowledge graph, error database, style guardian, DCCE text validator, mesh for multi-machine sessions, and a few more. The full list is in docs/TOOLS_REGISTRY.md; 37 tools are registered by src/index.ts.

Documentation is split by reader. For the agent: M7-GUIDE.md (operational guide) and Methodology/CURSOR_METHODOLOGY_RULES.md