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., "@AI-SDLC-MCP-Server audit the code quality of my Python 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.
This is a personal learning project for exploring Model Context Protocol (MCP), static code analysis, QA automation, and practical SDLC quality gates.
It is designed to be a small, local-first reference implementation that can be used from Cursor, Claude Desktop, or Claude Code while experimenting with developer workflow automation.
Tool | Outcome |
| Static Python/TypeScript security and reliability findings. |
| Executable async Playwright/Pytest test source. |
| 0-100 repository quality score and recommendations. |
Related MCP server: checkyourself
python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python -m src.server
python -m pytest -qCopy mcp_config.json into Cursor or Claude Desktop's mcpServers configuration and replace cwd with this repository's absolute path. Claude Code can use the same command: python -m src.server.
After connecting the server, capture one successful interaction for each tool and save the assets under docs/assets/.
The server performs local static analysis only. It does not execute target code, need credentials, call an LLM, or use a network API.
This server cannot be deployed
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