project-managment

The MCP Project Management Server provides a comprehensive set of AI-assisted tools to analyze, measure, document, and manage software projects. It exposes capabilities for project structure analysis, code metric computation, automatic documentation generation, and Git-related operations such as commit analysis, branch handling, and diff reviews. By running the server locally, you can query these tools through MCP-compatible interfaces and have AI assistants produce structured reports, visualizations, and ready-to-use artifacts like READMEs and API docs. This makes it suitable for software teams aiming to improve code quality, transparency, and collaboration around project governance.

Typical workflows include analyzing a project's structure to understand module boundaries and dependencies, generating a dependency graph to visualize relationships, computing code metrics such as complexity and test coverage, and generating documentation assets like READMEs and API docs. The server also supports generating architecture diagrams, changelogs, and quality reports, as well as carrying out linting and best-practices checks. When integrated with tools like Cursor, Claude, or other MCP clients, you can issue natural-language commands (e.g., "+analyze_project_structure+", "+generate_metrics_report+", "+generate_readme+") and receive structured results that can be consumed by other parts of your development workflow.

Prerequisites:

• Node.js 18 or higher
• npm or yarn
• TypeScript (dev dependency in the project)
• Git (for Git-related operations)

Installation steps:

1. Clone the repository

git clone https://github.com/code-alchemist01/project-managment-mcp-Server.git
cd project-managment-mcp-Server

2. Install dependencies

npm install

3. Build the project

npm run build

4. Run tests (optional but recommended)

npm test

5. Start the MCP server

npm start

Notes:

• Ensure your environment has permission to execute Node binaries and access the file system as the MCP server may read project trees and generate reports.
• If you customize paths, adjust the dist/index.js entry accordingly in your deployment configuration.

Tips and common considerations:

• Cursor integration: To use this MCP server with Cursor, create or edit the Cursor MCP config to point to the local Node server (e.g., command: node, args: [dist/index.js]). Ensure the path matches your built output location.
• Environment variables: If you enable authentication, logging, or storage backends, document and set variables such as API keys, file storage paths, and log levels in your deployment environment.
• Output formats: The server supports multiple output formats (JSON, Markdown, HTML, SVG/PNG, CSV). Plan which formats you need for reports and dashboards and configure generators accordingly.
• Performance: For large projects, consider running the server with increased memory limits and using incremental analyses to avoid re-running expensive analyses.
• Troubleshooting: If the MCP server fails to start, verify that dist/index.js exists after build and that port configuration (if applicable) is not blocked by a firewall.