A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.
Node.js server implementing Model Context Protocol (MCP) for filesystem operations, allowing AI systems to read, write, edit files and manage directories within specified allowed paths.
An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.
Enables code execution in isolated Docker containers with persistent IPython, Node.js, or R kernels, supporting file import/export and cross-session transfers via MCP tools.
Analyzes codebases to generate dependency graphs and architectural insights across multiple programming languages, helping developers understand code structure and validate against architectural rules.
An MCP server that provides tools for getting second opinions or detailed code reviews from Gemini 2.5 Pro by embedding file contents in prompts, allowing it to handle large codebases with extensive context.
Enables dynamic creation and execution of custom tools/functions in multiple programming languages at runtime, exposing them to MCP clients like Claude.
A comprehensive MCP server providing tools for AI agents to interact with code, including reading symbols, importing modules, replacing text, and sending OS notifications.
An MCP server implementing AI-user confirmation protocols, providing tools for LLMs to seek user confirmation when uncertain through yes/no questions, action confirmations, intent clarification, understanding verification, and satisfaction ratings.
An MCP server implementation that helps Claude remember and learn from programming errors by storing, retrieving, and finding similar errors with full context.
A Model Context Protocol (MCP) server for coordinating work between multiple AI agents in monorepos and shared codebases using a simple file-based signaling system or Discord thread chat.
Extracts minimal, relevant code context from multiple programming languages while analyzing diffs and optimizing imports to reduce token usage for AI assistants. Supports TypeScript/JavaScript, Python, Go, and Rust with token-aware caching.
A lightweight MCP server that enables AI agents to perform deep codebase analysis by leveraging Gemini's massive context window for cross-file analysis and intelligent file selection.
Enables AI agents to code and debug Commodore PET software using the VICE emulator, with CLI and MCP tools for session control, screen reading, memory manipulation, and testing.
A MCP server for managing and storing code snippets in various programming languages, allowing users to create, list, and delete snippets via a standardized interface.