thehardwarestore.ai

This is the official Tableau MCP server, a TypeScript application that bridges AI assistants with Tableau Cloud, Tableau Server, and Tableau Desktop via the Model Context Protocol (MCP). It exposes a comprehensive set of tools covering content exploration (workbooks, views, datasources), administration (users, flows, extract refresh tasks, jobs, license reclamation), advanced analytics (Pulse metrics, insights, knowledge graph), and human-in-the-loop confirmation workflows. The server is available in three variants: a web variant running over HTTP with OAuth 2.1 support (PKCE, token rotation, client registration), a desktop variant communicating with the local Tableau Desktop Agent over stdio, and a combined variant. Its architecture is modular: a core server layer registers tools and prompts against an MCP SDK instance, tools delegate to a Zodios-based Tableau REST API client or the desktop agent API, and cross-cutting concerns like authentication, feature gates, telemetry, and session stores are provided through pluggable provider interfaces. The codebase demonstrates strong engineering practices with extensive unit/e2e tests, typed schemas via Zod, consistent error handling patterns, and a clear separation between web and desktop concerns.

Prompt Optimizer is a multi-platform AI prompt optimization tool that helps users craft better prompts for large language models (LLMs) and image generation models. The system supports web (Vue 3 + Naive UI), desktop (Electron), Chrome extension, MCP server, and Docker deployment via a pnpm monorepo. Core workflows include system/user prompt optimization, evaluation with structured compare (pair-judge, synthesis), image generation (text2image, image2image, multi-image), variable extraction/value generation, a template system with 40+ built-in bilingual (zh/en) templates, and smart favorites with resource-aware asset management. The architecture cleanly separates framework-agnostic core services (LLM adapters, evaluation, storage, template processing) from UI composables and Electron proxies, using adapter, registry, and factory patterns extensively. A notable strength is the comprehensive test infrastructure spanning unit (Vitest), integration (real LLM API helpers gated by environment flags), and E2E (Playwright with VCR record/replay). Weaknesses include significant template duplication (bilingual files maintained separately), IPC fragility between Electron main/renderer, provider adapter maintenance overhead (16+ text adapters, 6+ image adapters), and some dead/deprecated code paths carrying legacy migration layers.

This repository provides a stealth browser automation MCP (Model Context Protocol) server that offers undetectable control over real browser instances (Chrome, Chromium, Edge) via the Chrome DevTools Protocol (CDP) and the `nodriver` library. It exposes 97 tools across 11 sections including navigation, DOM interaction, element extraction (styles, structure, events, animations), network interception and modification (including dynamic Python-powered hooks), CDP command execution, cookie management, tab control, and proxy forwarding with authentication. The architecture is a FastMCP-based server with singleton managers for browser instances, network interception, element cloning, and process cleanup. It supports stdio and HTTP transports, Docker deployment, and optional bearer auth. The system is designed to bypass anti-bot detection (Cloudflare, CreepJS, login walls) and is used for scraping, testing, and AI agent automation. While the codebase is well-structured and the functionality is comprehensive, there is some code duplication across multiple element cloner implementations and technical drift risk due to tight coupling with CDP APIs and the `py2js` transpiler dependency.

CtxPort is a browser extension and companion documentation website that lets users copy AI conversations (from ChatGPT, Claude, Gemini, DeepSeek, Grok, Doubao, GitHub) as structured Markdown Context Bundles. It solves 'context rot' by enabling portable, lossless migration of conversations between AI tools without uploading data to third parties. The architecture is a modern monorepo using pnpm workspaces and Turborepo, with separate packages for schema validation (Zod), markdown serialization, plugin-based extraction, and shared UI components. The browser extension (Manifest V3, WXT, React 19, Tailwind CSS 4) injects copy buttons and sidebar list icons into supported platforms, while the documentation website (Next.js + Nextra) provides multilingual (en/zh) user guides and feature descriptions. All processing is client-side; no data is uploaded. The codebase demonstrates strong engineering practices including a plugin system for extensibility, a strategy pattern for content flatteners, clean separation of concerns, and comprehensive CI/CD pipelines. Some fragility exists in DOM selectors and platform APIs, but the architecture is well-structured for adaptation.

GitMCP is a remote Model Context Protocol (MCP) server running on Cloudflare Workers that turns any public GitHub repository (or GitHub Pages site) into live, searchable documentation for AI tools. AI clients like Claude Desktop, Cursor, and MCP Inspector connect to gitmcp.io/{owner}/{repo} over SSE with OAuth and receive repo-specific MCP tools: fetch_<repo>_documentation, search_<repo>_documentation, search_<repo>_code, and fetch_url_content. The ingestion pipeline fetches llms.txt first (falling back to README.md, then landing-page HTML converted via html-to-md), chunks it with markdown-aware chunkers (heading-context-preserving, code-block and list aware), and indexes it into Cloudflare Vectorize using a dependency-free deterministic embedder built from hashing plus term-frequency/keyword extraction, with a hybrid ranking that blends normalized vector similarity (60%) with keyword/proximity/heading heuristics (40%). Robots.txt is parsed, cached, and respected before external fetches - an uncommon discipline for doc scrapers. The codebase also ships a full web chat application (React Router v7, Tailwind, shadcn/ui, AI SDK) with model selection across OpenAI/Anthropic/Groq/XAI, MCP server management, and a streaming chat API endpoint. Architecturally, the MCP server itself is implemented as a Cloudflare Durable Object (MyMCP), with KV caching (TTL with jitter to prevent cache stampedes), R2 storage for pre-generated docs, a queue for background ingestion, Vectorize for semantic search, Analytics Engine for usage metrics, and GitHub API integration with rate-limit tracking and retry/backoff. A notable design pattern is the RepoHandler strategy interface: per-repo families (three.js, react-router) provide customized tool sets, doc-fetch strategies, and search behavior while the generic handler covers all other repos, including a static mapping of library names to their canonical doc repos. Testing is solid: Vitest unit tests cover tool registration, repo handlers, URL parsing, caching, and three.js utilities (with snapshots), and Playwright e2e tests run against a locally-provisioned R2 environment; CI runs both on push/PR. Weaknesses and drift: the embedding layer is explicitly a prototype-grade stand-in ('In a production environment, you would use a proper embedding service like OpenAI') and will produce weak semantic results; badge.ts contains ~96k tokens of hardcoded escaped SVG logo paths (extreme maintenance fragility); several hardcoded host URLs (git-mcp.idosalomon.workers.dev, CHAT_API_URL) baked into source; a generated-type mismatch where Vectorize metadata filters use $gt at runtime but generated types only declare $eq/$ne; vector re-ingestion only deletes the first 100 stale chunks (repos with more chunks accumulate orphans); and the frontend uses React Router v7's unstable_viteEnvironmentApi. These are prototype-velocity marks rather than architectural rot - the core strategy/caching/testing structure is genuinely well-built.

This repository is a collection of Agent Skills for AI coding assistants (Claude Code, Codex, OpenCode) that enables them to interact with Obsidian vaults. It contains six skill definitions: obsidian-markdown (creating Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties), obsidian-bases (creating database-like .base files with views, filters, formulas), json-canvas (creating visual .canvas files with nodes and edges), obsidian-cli (interacting with Obsidian via CLI for note management and plugin development), defuddle (extracting clean markdown from web pages), and knap (rendering Markdown templates from structured data). Each skill is a self-contained markdown file following the Agent Skills specification, providing detailed syntax, examples, and validation steps. The repository is purely documentation with no executable code, serving as a knowledge base that AI agents use to perform Obsidian-related tasks on behalf of users.

This is a Model Context Protocol (MCP) server that enables AI agents to interact with the Godot game engine. It provides tools to launch the Godot editor, run projects in debug mode, capture console output, manage scenes, add nodes, load sprites, export mesh libraries, and handle UID references for Godot 4.4+. The server is a single TypeScript process that communicates via stdio, spawning Godot processes for complex operations and using a bundled GDScript for scene manipulation. It includes path traversal protection, class name validation to prevent arbitrary script instantiation, and flexible parameter naming (supports both camelCase and snake_case). The project is well-structured, with clear error handling and debug logging, making it suitable for integration with AI coding assistants like Claude, Cline, and Cursor.

Lunar Tools is a Python library (v0.4) providing a modular toolkit for building interactive exhibitions. It bundles capabilities for audio recording/playback, speech-to-text and text-to-speech (OpenAI, ElevenLabs, Deepgram), real-time voice interaction via OpenAI Realtime API, webcam capture, MIDI controller input, multi-backend display rendering (OpenGL, OpenCV, Pygame, SDL/CUDA), and inter-process communication through OSC, ZMQ, and WebRTC (data channels and audio streaming). Additionally, it includes wrappers for image generation APIs (DALL-E 3, SDXL Turbo/Replicate, Flux on fal.ai) and LLM interfaces (OpenAI, Gemini, DeepSeek). The library is designed for quick prototyping of interactive installations, with example scripts in the `examples/` directory that demonstrate each module. The codebase is a collection of independent submodules rather than a monolith, each with its own set of dependencies. The most technically interesting parts are the WebRTC communication layer (`lunar_tools/comms/webrtc.py`), which handles SDP exchange via a local REST signaling server, supports automatic reconnection, and includes Opus codec tuning for high-quality audio. The custom codec (`lunar_tools/comms/webrtc_codec.py`) for encoding numpy arrays and messages into WebRTC data channels is a clean, reusable component. The ZMQ pair endpoint (`lunar_tools/comms/zmq.py`) offers a simple bidirectional JSON/image/audio channel. However, many modules suffer from inconsistent error handling, lack of type hints, and tight coupling to logging utilities. The overall architecture is pragmatic but not exemplary; it serves its purpose as a glue library for exhibition software. The repository includes a test suite (pytest) covering individual modules, CI/CD for PyPI publishing, and basic documentation in the README. Maintenance appears active (last commit within a month, though the date may be anomalous). It carries a BSD-3 license and has 296 GitHub stars, suggesting moderate community interest.

Vizra ADK is a retired Laravel package (final release v0.0.48) that provides a comprehensive framework for building AI agents with LLM orchestration, tool systems, memory, workflows, evaluations, tracing, and MCP integration. It is not a standalone application but a library designed to be installed into Laravel projects. The architecture follows Laravel package conventions: service providers, facades (Agent, Workflow), Eloquent models, Artisan commands, Livewire components, and database migrations. Key entry points include the Agent facade for running agents, the Workflow facade for orchestrating multi-agent workflows, and a set of CLI generators for scaffolding agents, tools, and evaluations. The system supports multiple LLM providers via Prism PHP, persistent memory (session, user, vector), tool chaining, sub-agent delegation, and an evaluation framework with LLM-as-a-Judge assertions. The codebase is well-structured with strong test coverage (Pest + Mockery), but the package is officially retired with no further development, leading to high technical drift as dependencies age out. Despite retirement, many components (e.g., ToolChain, VersionablePrompts, AgentVectorProxy, HasLogging) are self-contained and demonstrate reusable patterns suitable for extraction into new projects.