Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Agentic Software Factory MCP ask the software architect to propose a scalable architecture for my project"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
10 specialist agents (PM, Architect, Backend, Frontend, Database, AI, Security, QA, DevOps, Code Reviewer) as a local MCP server.
Your code stays on your machine.
p.s. If this saves you time, star the repo - it helps others find it.
AI coding tools are powerful, but they often answer as a generic assistant.
This MCP gives you a role-based factory inside Cursor (or Claude Desktop): PM, Architect, Backend, Frontend, Database, AI, Security, QA, DevOps, and Code Reviewer - all local, via Docker or Python.
Related MCP server: Claude Team MCP
git clone https://github.com/tresor228/agentic-software-factory-mcp.git
cd agentic-software-factory-mcp
./scripts/install-docker.shWindows PowerShell:
.\scripts\install-docker.ps1Paste the printed JSON into Cursor → Settings → MCP
Restart Cursor
Confirm the server is green: agentic-software-factory → 7 tools, 10 prompts, 4 resources
Works on Linux, macOS, and Windows (Docker Desktop).
./scripts/install.sh # Python 3.11+
./scripts/install-uv.sh # no system PythonSee docs/installation.md.
Important: in Cursor, agents are MCP prompts, not @mentions.
@ usually lists files/docs. Your agents appear in the MCP prompts menu or via / (depends on Cursor version).
Example flow:
1. Prompt: software_architect
Task: Analyze this repo and propose a scalable architecture.
2. Prompt: database_architect
Task: Design the schema from existing models.
3. Prompt: code_reviewer
Task: Review the latest implementation for bugs and security.Full guide: docs/usage.md · Agent list: AGENTS.md
Agent | What you get |
| Specs, stories, prioritization |
| Architecture, boundaries, trade-offs |
| APIs, services, server design |
| UI structure, UX flows |
| Schemas & migrations from code |
| LLM / RAG pipelines |
| Threats & hardening |
| Test strategy & edge cases |
| CI/CD & observability |
| Quality + security review |
Tools: filesystem (read/list/search) · GitHub (public without token)
Knowledge: coding, architecture, security, database rules as MCP resources
Config: none required to start
Default mount is ~/Projects when it exists; otherwise home (with a warning).
Prefer mounting a projects folder, not your full home (.ssh, .aws, …).
Never paste a GitHub PAT in chat. Use GITHUB_TOKEN in the MCP process env only if you need private repos.
src/server.py MCP entrypoint (stdio)
src/agents/ 10 role prompts
src/tools/ filesystem + GitHub
src/knowledge/ shared rules (resources)
scripts/ Docker / Python / uv installers
docs/ installation + usageIf you use this daily:
Star the repo so more builders find it
Open issues for bugs / ideas
PRs welcome - see CONTRIBUTING.md
Built for the open-source MCP ecosystem. MIT licensed.
Edition | Status |
Community (local Docker / Python) | Available now |
Enterprise (hosted MCP, team memory) | Planned |
MIT © TresorAlad
This server cannot be deployed
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