"A server for file search, project analysis, and code refactoring on macOS" matching MCP servers:

Exposes structured prompt generation tools for refactoring protocol and governance specifications via MCP, enabling teams to generate, validate, and retrieve standardized refactoring prompts through automation scripts and HTTP APIs.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Provides comprehensive code analysis through three MCP servers: static analysis for code quality and security, dependency analysis for package management and vulnerabilities, and complexity analysis for maintainability assessment across multiple programming languages.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.

MCP server for persistent project knowledge management, enabling AI assistants to store, search, and reuse project analysis insights with freshness checking and impact analysis.

Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Enables AI assistants to manage project analysis, code metrics, documentation, Git operations, code quality, and file organization through natural language commands.

Enables multi-project code analysis by invoking project-provided Claude Code agents and skills, with optional SVN updates, returning Markdown reports to MCP clients.

Provides AI coding agents with structured access to indexed codebases via semantic search, symbol analysis, and file reading tools.

Enables LLMs to perform high-performance code search and analysis across multiple languages using symbol indexing, regex text search, and structural AST pattern matching. It also provides tools for technology stack detection and dependency analysis with persistent caching for optimized performance.

Provides a Model Context Protocol server for executing AppleScript and JavaScript for Automation scripts on macOS, featuring a knowledge base of pre-defined scripts and supporting automation of macOS applications and system functions.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables interaction with macOS system features including file dialogs, clipboard management, Finder integration, and system notifications. It also provides tools for taking screenshots, processing images, managing PDFs, and interacting with the Apple Notes app.

Provides advanced system monitoring and file search capabilities for macOS, allowing users to track performance metrics and perform enhanced file searches with content analysis and tagging features.

Control macOS system settings, apps, windows, audio, displays, screenshots, and Focus mode via MCP.

Enables AI assistants to read, create, update, and delete data in macOS apps (Reminders, Calendar, Notes, Mail, Messages, Contacts) via natural language, using EventKit, JXA, and SQLite backends.

A self-hosted remote MCP server for cross-model AI session handoff, allowing users to start a project in one AI assistant and continue in another via shared project logs, files with version history, and full-text search, all stored in a single SQLite file on their own server.