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Expose codebase structure to LLMs while drastically reducing context token usage.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "@penqwin/mcp" yet — see the docs or source repo.
Use @penqwin/mcp to analyze this repository and output the main directories, core modules, and structural relationships between key classes and functions with minimal context.
A compact codebase structure summary that helps quickly understand project composition and module relationships.
I want an LLM to help modify this project without sending the full source. First use @penqwin/mcp to generate an AST-based skeleton, then identify the files and functions related to the user authentication flow.
A task-oriented structural skeleton plus identified authentication-related code areas for more precise follow-up prompts.
Use @penqwin/mcp to inspect this codebase structure and analyze the likely upstream call sites, downstream dependencies, and public interfaces affected by refactoring the payment module.
A structured refactor impact list to help assess risks and plan implementation.
Provide AI agents with developer utilities like code review, JSON formatting, and password generation.
Gives AI coding agents a structural map of your repository fast.
Index source code locally to query symbols, dependencies, and tree structure.
Help AI agents navigate, search, and understand codebases and change history.
Turn an MCP-aware IDE into an intelligent agentic coding assistant.
Build, debug, and manage software tasks with natural language across LLMs.