#codebase

Automatically generates a full MCP server codebase (Node/Python/Java) using the validated schema, outputting modular files for CLI-based execution or Claude Desktop connection.

CodeCompass: AI-powered Vibe Coding with MCP. Connects Git repositories to AI assistants like Claude, using Ollama for privacy or OpenAI for cloud. Integrates with VSCode, Cursor, and more.

Persistent codebase intelligence that gives agents memory across sessions.

MCP server implementation for PocketFlow - A codebase analysis and tutorial generation tool

MCP server to empower your agent with enterprise-grade Gemini integration for codebase analysis, live search, text/PDF/image processing, and more on your favorite MCP clients.

A set of tools to use while coding using the Claude code cli. "smartgrep" is a grep-like tool that uses a semantic index of the codebase to provide Claudes with information tailored for how they think. Claudes love it! A "codebase-curator" Claude to assist the "developer" Claude in his coding tasks, together they implement fully integrated code.

The portable alternative to Cursor Rules and IDE-specific rules. Transform your project documentation into intelligent AI context using standard Markdown files that work across any MCP-compatible AI tool. Escape vendor lock-in and scattered documentation forever.

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.

Give AI agents a visual architecture canvas — build, read, and modify system design diagrams from Claude Code, Cursor, or any MCP client.

Save tokens while coding — your AI agent gets structured code context, not file dumps. CodeWeave is an MCP server that gives AI agents structured code intelligence instead of dumping entire files into context. Your agent queries local indexes — AST, call graph, type graph, and hybrid semantic search — and gets back only what it needs. The result: less token waste, more relevant context, better coding decisions. It supports 7 languages (Python, TypeScript, JavaScript, Go, Rust, Java, C#) with a 6-stage hybrid search pipeline combining vector embeddings, full-text search, and structural density scoring. Everything runs locally with zero external dependencies beyond Ollama for embeddings. Monorepo and git worktree support included. A file watcher keeps indexes current as code changes — no manual reindexing needed. ATTENTION: Run "npx @codeweave/mcp" in your project directory first. The setup wizard handles everything for you. At first, it may take some time. INFO: Exhaustively tested on large Java, C#, JavaScript, TypeScript, React codebases. Contributions and feedback are welcome.