An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.
Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.
Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.
Windows-first MCP server for deterministic file writes with atomic create, replace, and structured edit operations, preserving encoding and newline styles.
Enables AI models to perform file system operations (reading, creating, and listing files) on a local file system through a standardized Model Context Protocol interface.
An MCP server that provides a flexible lens into directory structures and files, enabling LLM clients to efficiently navigate and understand codebases with minimal noise. It offers secure, gitignore-aware file access with tools like directory listing, file reading, and grep-like search.
Provides component information, usage guidelines, and code generation for AWS Cloudscape Design System in React, with search and pattern library capabilities.
A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and fetching weather data to demonstrate basic tool implementation and API integration patterns.
A beginner MCP server that enables Claude to read local text, CSV, and Markdown files. Built as a learning project to understand how to connect AI to local file systems using the MCP protocol.
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.