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Nogueira"},"license":"AGPL-3.0-or-later","homepage":"https://github.com/DiegoNogueiraDev/mcp-graph-workflow#readme","keywords":["mcp","prd","task-graph","local-first","agent-tooling"],"repository":{"url":"git+https://github.com/DiegoNogueiraDev/mcp-graph-workflow.git","type":"git"},"description":"AI-Driven Software Engineering, local-first. PRD vira grafo persistente, TDD obrigatório, contexto sobrevive ao reload. AGPL v3.","maintainers":[{"name":"diegonogueiradev_","email":"diegonogueiradev@gmail.com"}],"readme":"<!--\n * SPDX-License-Identifier: AGPL-3.0-or-later\n * Copyright © 2026 Diego Lima Nogueira de Paula\n-->\n\n<p align=\"center\">\n  <img src=\"docs/images/graph-logo.jpg\" alt=\"mcp-graph\" width=\"600\">\n</p>\n\n<h1 align=\"center\">mcp-graph</h1>\n\n<p align=\"center\">\n  <a href=\"https://github.com/DiegoNogueiraDev/mcp-graph-workflow/actions/workflows/ci.yml\"><img src=\"https://github.com/DiegoNogueiraDev/mcp-graph-workflow/actions/workflows/ci.yml/badge.svg\" alt=\"CI\"></a>\n  <a href=\"https://www.npmjs.com/package/@mcp-graph-workflow/mcp-graph\"><img src=\"https://img.shields.io/npm/v/%40mcp-graph-workflow%2Fmcp-graph\" alt=\"npm\"></a>\n  <a href=\"LICENSE\"><img src=\"https://img.shields.io/badge/License-AGPL_v3-blue.svg\" alt=\"AGPL v3\"></a>\n  <a href=\"COMMERCIAL.md\"><img src=\"https://img.shields.io/badge/Commercial-available-informational\" alt=\"Commercial license\"></a>\n</p>\n\n---\n\n**mcp-graph is an MCP server** — a persistent context layer that plugs into the AI coding tool you already use and gives it structured memory, task tracking, and engineering discipline.\n\nThe AI does the work. The server keeps it on track.\n\n---\n\n## The problem\n\nEvery AI coding session has the same failure modes:\n\n- **The agent forgets** — each new chat starts from zero, the plan gets reinvented\n- **PRDs become walls of text** — nobody re-reads them, the agent improvises\n- **No traceability** — you can't tell what was done, what's blocked, or why\n\n## How it works\n\nmcp-graph runs as an MCP server that your AI coding tool connects to automatically. Once connected, the AI gains access to 54 MCP tools — persistent task graph, compressed context, lifecycle phases, and structured engineering.\n\nYou write a PRD. The AI implements it.\n\n```\nPRD → import_prd (MCP) → task graph → start_task → [TDD] → finish_task → done\n```\n\nThe AI calls the MCP tools directly. No manual commands needed.\n\n## Setup — once per project\n\n```bash\nnpm install -g @mcp-graph-workflow/mcp-graph\nmcp-graph init\n```\n\n`mcp-graph init` generates the connection config for every supported AI tool automatically:\n\n| AI Tool | Config file generated |\n|---|---|\n| Claude Code | `.mcp.json` |\n| OpenCode | `opencode.json` |\n| GitHub Copilot (VS Code) | `.vscode/mcp.json` |\n| Cursor | `.mcp.json` |\n\nOpen your AI tool. It connects to the MCP server automatically. No further configuration needed.\n\n**Prerequisites:** Node.js ≥ 18. No Docker. No LLM API key.\n\n## Tell the AI what to do\n\nOnce connected, give your AI tool a PRD and a skill:\n\n> \"Read PRD.md and implement it using /graph-implement\"\n\nThe AI will:\n1. Call `import_prd` (MCP) — parses the PRD, creates tasks with acceptance criteria in the graph\n2. Call `start_task` (MCP) — picks the next task with compressed context (73% fewer tokens)\n3. Follow TDD: write failing test → implement → make it pass\n4. Call `finish_task` (MCP) — validates 9 Definition-of-Done checks, marks done, suggests next\n5. Repeat until the backlog is empty\n\n## Skills — your interface to the AI\n\nSkills are slash commands that trigger the right MCP workflow for each phase:\n\n| You want to... | Skill |\n|---|---|\n| Implement the next task | `/graph-implement` |\n| Validate what was built | `/graph-validate` |\n| Plan a sprint | `/graph-plan` |\n| Review before PR | `/graph-review` |\n| Import a PRD | `/graph-prd` |\n\nSkills are installed by `mcp-graph init` and appear in the autocomplete of your AI tool (type `/` in Claude Code, OpenCode, or Copilot).\n\n## What the AI gains via MCP\n\n| Capability | What it does |\n|---|---|\n| Persistent task graph | SQLite local — survives across sessions, never in the cloud |\n| Compressed context | 73% token reduction per task — RAG (FTS5 + BM25), no external LLM |\n| 9-phase lifecycle | ANALYZE → DESIGN → PLAN → IMPLEMENT → VALIDATE → REVIEW → HANDOFF → DEPLOY → LISTENING |\n| DoD automation | 9 checks per task before marking done (AC quality, TDD, no blockers) |\n| Knowledge store | Captures decisions, patterns, and context for future sessions |\n\n## Privacy\n\nZero telemetry. Zero phone-home. Runs offline after installation.\n\nAll data lives in `workflow-graph/graph.db` — local SQLite, gitignored, never in the cloud.\n\n## Documentation\n\n- [Quickstart](docs/getting-started/QUICKSTART.md) — connect and run in 60 seconds\n- [AI tools setup](docs/getting-started/ai-tools-setup.md) — Claude Code, OpenCode, Copilot, Cursor\n- [Full guide](docs/getting-started/GUIDE.md) — MCP server, skills, lifecycle phases\n- [End-to-end tutorial](docs/getting-started/end-to-end-tutorial.md) — PRD to delivery\n- [Cheatsheet](docs/getting-started/CHEATSHEET.md) — all commands and skills\n\n## License\n\n[AGPL-3.0-or-later](LICENSE) — open source with strong copyleft.\nProprietary commercial use: [commercial license available](COMMERCIAL.md).\n","readmeFilename":"README.md"}