This repository contains a VS Code extension that acts as a bridge between the Antigravity IDE and a mobile device, enabling remote interaction via a local HTTPS server. The extension starts a self-signed HTTPS server on the LAN, uses the Chrome DevTools Protocol (CDP) to inspect and manipulate Antigravity IDE instances, and leverages the Language Server Protocol (LSP) to monitor and control code generation processes. A companion mobile web interface, served by the local server, provides real-time chat mirroring, message sending, file uploads, voice input, and generation control through a WebSocket connection. The architecture consists of three main parts: the VS Code extension (managing the server lifecycle and IDE integrations), the Express/WebSocket server (handling API requests, real-time communication, and static file serving), and the mobile SPA (rendering the IDE state and forwarding user interactions). Key capabilities include QR-code based pairing, end-to-end encrypted local communication, multi-instance switching, and programmatic control via an MCP server that exposes the API over stdio JSON-RPC. The extension demonstrates solid engineering practices with TypeScript, modular service layers, middleware for authentication and security, and a test suite covering core functionality. While tightly coupled to Antigravity's UI and protocols by necessity, it isolates concerns through well-defined services (CDP, LSP, antigravity interactions) and reusable patterns such as the CSP middleware, CDP wrapper, and stdio JSON-RPC proxy. Ongoing maintenance addresses selector fragility and API updates, ensuring compatibility with evolving IDE and VS Code versions.
Chiasmus is a neurosymbolic verification engine packaged as a TypeScript library and Model Context Protocol (MCP) server. It bridges LLMs with formal solvers — Z3 (SMT) and SWI-Prolog (WASM) — by translating natural-language problems into template-based formal specifications that are verified with mathematical certainty. It also performs static code analysis: tree-sitter-based extraction of call graphs for TypeScript/JavaScript, Python, Go, Rust, Clojure, Scheme, Racket, and Common Lisp, followed by 16 analyses (reachability, dead code, impact, cycles, layer violations, community detection, hubs, bridges, diffs). Architecturally, the repo is a modular ESM library with an MCP server entry point (src/mcp-server.ts) exposing 11 tools over stdio (chiasmus_graph, chiasmus_map, chiasmus_formalize, chiasmus_verify, chiasmus_search, chiasmus_review, chiasmus_learn, etc.). Core subsystems: graph extraction with per-file content-hash caching and snapshot diffing; a formalization engine that selects from 8 built-in skill templates via BM25/embedding retrieval and runs an LLM-driven correction loop against the solvers; a SQLite-backed skill library that learns new templates from verified solutions; and pluggable LLM adapters (Anthropic, OpenAI-compatible, Azure, local via node-llama-cpp). Data flows: source files are parsed by tree-sitter into a CodeGraph, which can be emitted as Prolog facts for symbolic queries or analyzed natively with O(V+E) algorithms (Tarjan SCC, BFS). For verification, a problem statement is embedded, matched to a template, slot-filled by an LLM, linted, and iteratively corrected until the Z3/Prolog solver confirms the property or finds a counterexample. The benchmark suite compares Chiasmus against traditional implementations across RBAC, dependency resolution, taint analysis, workflow validation, and validation consistency. Maintenance is active (v0.1.28, CI on Node 22/24, 70+ test files covering graph extraction, solvers, MCP tools, search, and skills). The codebase is well-engineered with several genuinely reusable components — native graph algorithms, an LRU extraction cache, a BM25 index, and a tsconfig alias resolver — though some coupling exists between the extractor and the TypeScript type environment, and the vendored WASM grammars require periodic upstream syncs.
Playwright MCP is the npm distribution package for Microsoft's Model Context Protocol server that exposes Playwright browser automation to LLM clients as MCP tools. Despite the large star count, this repository contains almost no product source: the actual MCP tool implementation (browser_click, browser_navigate, browser_snapshot, network routing, storage, vision, PDF, devtools, and testing capabilities) lives in the Playwright monorepo at packages/playwright-core/src/tools/mcp. cli.js and index.js are thin shims that re-export tools.decorateMCPCommand and tools.createConnection from playwright-core's internal bundles, and src/README.md openly states where the source is. This repo functions as the release-shipping wrapper: it pins a Playwright alpha build, rolls it forward via roll.js, regenerates README tool/config documentation from the bundled tool schemas via update-readme.js, publishes the @playwright/mcp npm package (GitHub Actions OIDC, Azure ESRP, and the MCP Registry with server.json/package.json version validation), and builds multi-arch Docker images. Classified as a pure application, not data_research.
Cognee is an open-source AI memory platform that enables agents and applications to build, store, and query persistent knowledge graphs from documents, code, and conversations. The system provides a Python SDK, a FastAPI REST API, a CLI, a Next.js frontend, and an MCP server for agent integration. Core operations include ingesting data (remember), extracting entities and relationships via LLMs or GLiNER (cognify), retrieving context (recall), and self-improvement loops (improve). The architecture is modular with adapter/registry patterns for graph databases (Ladybug, Neo4j, Turso), vector stores (LanceDB, PGVector), LLM providers (LiteLLM, OpenAI, Anthropic, Ollama), and integrations (Slack, GitHub, Google Drive). The frontend provides a dashboard for dataset management, knowledge graph visualization, and business analytics. The project also includes an evaluation framework for benchmarking retrieval strategies against standard datasets like HotpotQA and BEAM. While production-grade in many areas, some components are marked as demo-quality or have cloud-only stubs.
Serena is a production-grade MCP (Model Context Protocol) server that provides AI coding agents with deep code intelligence across 40+ programming languages. It acts as an 'IDE for your agent', offering symbol-level retrieval, editing, refactoring, and debugging capabilities by orchestrating Language Server Protocol (LSP) backends. The system is designed to integrate with AI clients like Claude Code, Codex, Copilot, and Grok, exposing a rich set of tools for file manipulation, symbol navigation, memory persistence, and project configuration. Architecturally, Serena is a Python application with a CLI entry point that starts an MCP server, a Flask-based web dashboard for monitoring and configuration, and a hooks system for client-side pre-tool-use events. Its core is the `solidlsp` package, a custom synchronous LSP abstraction layer that manages the lifecycle of 40+ language servers (Python, TypeScript, Rust, Go, Java, etc.), handling initialization, capability negotiation, and request/response framing. The `interprompt` library provides a sandboxed Jinja templating engine for prompt generation, and the memory manager persists project-specific context across sessions. Key features include: multi-language symbol search, references, definitions, hover, rename, and diagnostics; a code editor abstraction supporting both LSP and JetBrains backends; a configurable tool registry with automatic discovery; and a comprehensive test suite with per-language integration tests. The project emphasizes reproducibility through pinned dependency versions and SHA256 verification for downloaded language server binaries, though this creates a maintenance burden as versions must be manually updated.
This repository implements an MCP (Model Context Protocol) server that renders Excalidraw diagrams with hand-drawn style animations. It provides two primary tools: `read_me` (returns a cheat sheet with element format and color palettes) and `create_view` (renders diagrams from Excalidraw JSON elements with streaming draw-on animations). The architecture is a Node.js server supporting both Streamable HTTP and stdio transports, with a React-based widget that uses Excalidraw's `exportToSvg` for SVG rendering (not the full canvas component). Key features include: progressive element streaming with morphdom DOM diffing, smooth viewport camera animation, interactive fullscreen editing with checkpoint/restore persistence (file, memory, or Redis backends), pencil stroke audio feedback, and one-click export to excalidraw.com. The server is deployable on Vercel via serverless functions. The codebase demonstrates strong engineering practices with clean abstractions, well-documented design decisions, and a focus on performance (no blink on final render, efficient SVG diffing).
This repository provides client-side integrations, tooling, and examples for Zep, a long-term memory platform for AI agents built on temporal knowledge graphs. It is not the Zep service itself but the ecosystem to use it. The codebase includes SDK packages for over a dozen AI agent frameworks (Google ADK, AutoGen, CrewAI, LangGraph, Pydantic AI, LiveKit, Mastra, Vercel AI SDK, Microsoft Agent Framework, Strands, AG2) in Python, TypeScript, and Go. Each integration follows a consistent pattern: out-of-band provisioning of users and threads (server-generated UUIDs), automatic context injection before each LLM call, and on-demand graph search tools. The repository also contains a standalone ingestion pipeline (zep-ingest) for bulk-loading historical data (Slack exports, emails, documents, transcripts) into Zep graphs, a Model Context Protocol (MCP) server exposing Zep graph tools, benchmarks (LoCoMo, LongMemEval) for evaluating long-term memory retrieval, and an evaluation harness for testing retrieval and question-answering accuracy. While primarily an application-focused repository, it includes data pipeline components and evaluation frameworks that give it a hybrid character.
This repository is a production-grade Model Context Protocol (MCP) server that bridges AI assistants to Figma. It exposes 100+ MCP tools for reading and manipulating Figma files, variables, components, styles, FigJam boards, Slides, version history, tokens, and accessibility audits. The architecture is dual-mode: a local Node.js server (`src/local.ts`) communicates over stdio to AI clients and via WebSocket to a bundled Figma Desktop Bridge plugin, while a Cloudflare Workers variant (`src/index.ts`) supports cloud sessions with OAuth and a Durable Object WebSocket relay. The Desktop Bridge is the primary data channel, with Figma REST API fallbacks for scenarios where the plugin is unavailable or limited to certain plans. Beyond basic Figma CRUD, the codebase contains substantial engineering investment in design-token synchronization: canonical DTCG-aligned token documents round-trip through parsers and formatters for CSS variables, Tailwind v3/v4, SCSS, TypeScript modules, Style Dictionary, and Tokens Studio. A separate extraction engine (`src/core/extraction/`) mines production codebases for styling conventions and scaffolds design-system packages, while a scoring engine produces weighted design-system health dashboards. These components give the repo a hybrid quality: it is fundamentally an application, but its token pipeline and codebase-analysis subsystems are data-engineering-like assets that could be reused independently. The codebase demonstrates strong engineering practices: strict Zod schemas, consistent tool registration factories, connector abstraction, extensive Jest test coverage, atomic file writes, adaptive response compression, and careful caching/invalidation. However, it also carries meaningful debt: some token parsers are unimplemented stubs, a few files are monolithic (`figma-tools.ts`, `src/local.ts`), local/cloud connector implementations must be kept in sync manually, and some documentation references outdated internal paths. Overall, this is a mature, feature-rich MCP server with several genuinely reusable components for anyone building design-tool pipelines, token bridges, or local process-lifecycle infrastructure.
This is an MCP (Model Context Protocol) server implementation that provides LLMs with MySQL database access. The server exposes a single `mysql_query` tool for executing SQL queries and resources for listing tables and column metadata. It supports both stdio and HTTP transport modes, the latter with bearer token authentication. Key features include multi-database mode (querying across schemas without reconnection), schema-specific write permissions (INSERT/UPDATE/DELETE/DDL per database), SSL/TLS with mTLS, Unix socket connections, and a sophisticated PII redaction system that combines column-name heuristics with regex value scanning and Luhn-verified credit card detection. The architecture is monolithic: a single Express server (or stdio process) manages a mysql2 connection pool and routes all MCP protocol requests through zod-validated schemas. The codebase is well-tested with unit, integration, and end-to-end test suites, and demonstrates careful engineering in areas like query parsing (using node-sql-parser), permission enforcement, and secure defaults (write operations disabled by default).
Spectrum UI is a comprehensive React component library built with Next.js, Tailwind CSS, and Framer Motion. It provides over 250 animated, accessible components that can be installed via CLI or copied directly into projects. The repository includes a documentation site, marketing pages, a pro/template dashboard, and an MCP server for AI-assisted component discovery and installation. The architecture follows a monorepo structure with shared utilities, a component registry, and separate sections for docs, marketing, and user-facing features. Key technical strengths include a robust class-merging utility, reusable animation presets, a live-preview documentation system, and well-abstracted UI primitives. However, the codebase exhibits some maintenance challenges due to dependency volatility, duplication between registry and source components, and tight coupling with external services like Supabase and PostHog.
ghqr is a Go-based CLI tool for scanning GitHub enterprises, organizations, and repositories against a comprehensive set of best-practice rules. It produces actionable reports in Markdown, Excel, and JSON formats, with prioritized findings, remediation plans, and manual checklists. The tool also includes an MCP (Model Context Protocol) server for AI assistant integration, enabling interactive scanning and recommendations via stdio or HTTP/SSE. A deterministic mock generator allows demos and testing without live API calls. The architecture follows a composable pipeline pattern: initialization, data loading (from live API or JSON replay), enterprise/GHES/organization/repository discovery and scanning, evaluation against YAML-defined rules, and report rendering. Scanners use both REST and GraphQL APIs with batch queries, rate-limit handling, and tri-state boolean support for GHES on-prem instances. The rule registry is embedded as YAML files, loaded at startup into a typed registry that evaluators reference by stable rule IDs. Key entry points are the `ghqr scan`, `ghqr mcp`, `ghqr mock`, and `ghqr list-recommendations` commands. External dependencies include the GitHub API (REST and GraphQL), the `excelize` library for Excel output, and the `mcp-go` library for MCP protocol support. The tool is containerized via Docker and published to GHCR, with install scripts for Linux, macOS, and Windows.
This repository provides a TypeScript SDK for the Dedalus API and an MCP (Model Context Protocol) server that exposes the API as MCP tools. The SDK is generated from an OpenAPI specification using Stainless, offering type-safe access to AI model functionalities including chat completions, embeddings, image generation, and audio processing. It includes features such as automatic retries with exponential backoff, streaming responses, file upload handling, and structured output parsing via Zod schemas. The MCP server implementation allows Dedalus API endpoints to be used as tools by MCP-compatible clients like Claude and Cursor. It includes a compatibility layer that transforms JSON schemas to accommodate various client limitations (e.g., handling unions, inlining references, truncating tool names). The server supports both stdio and HTTP transports and can be extended with custom tools. Key architectural elements include a resource-based client structure, a robust request/response transformation system, and a modular MCP server design. The codebase demonstrates strong engineering practices with comprehensive testing, active maintenance, and clear separation of concerns between the SDK and server components.
Langflow is a full-stack, open-source platform for building and deploying AI-powered agents and workflows. It provides a visual flow builder (React frontend), a Python backend (FastAPI/SQLModel), a CLI executor (LFX), an MCP server for AI tool integration, and a Python SDK. The codebase is a monorepo with multiple packages: the main `langflow` distribution, `langflow-base` (core backend), `lfx` (lightweight executor/CLI), `langflow-sdk` (API client), `langflow-stepflow` (step-based execution), and extension bundles (e.g., lfx-duckduckgo, lfx-arxiv, lfx-ibm, lfx-docling). The backend uses SQLModel with Alembic migrations, Celery for background tasks, FastAPI for REST endpoints, and a plugin/extension system for bundles. The frontend is a React/TypeScript app built with Vite, React Flow, Zustand, and Tailwind. The architecture supports multi-worker deployments, auto-login, JWT auth, rate limiting, MCP server integration, and a rich component library for LLMs, vector stores, tools, and data processing.
This is a monorepo containing over 40 independent MCP (Model Context Protocol) servers for AWS services, hosted under awslabs/mcp. Each server exposes tools, resources, and prompts that allow AI agents (coding assistants, chatbots) to interact with AWS APIs, databases, documentation, and best practices. The architecture is a collection of standalone Python packages, each with its own entry point (server.py/main.py), tests, Dockerfile, and PyPI distribution. Servers are built using either FastMCP or the lower-level MCP Python SDK, with consistent patterns for read-only enforcement, error handling, AWS client creation, and user-agent tracking. The codebase demonstrates solid engineering with extensive test coverage, but suffers from significant copy-paste boilerplate across servers (healthcheck scripts, Context singletons, test_init.py) and inconsistent use of MCP frameworks. Notable high-craftsmanship components include the AWS API MCP server's custom CLI-to-IR parser and execution engine, the OpenAPI MCP server's pluggable authentication provider framework, and the DynamoDB MCP server's schema-driven code generation tool. The repository is actively maintained with frequent commits and issue triage, though some servers are marked as deprecated or in initial setup, and the announcement of a successor ('Agent Toolkit for AWS') suggests potential future migration.
The AIMLInterviews repository is primarily an interview preparation tool that centers around an MCP (Model Context Protocol) server. This server turns any MCP-compatible AI assistant into a spoiler-free interview coach by providing tools to list problems, give progressive hints, review answers, and generate learning or company-specific preparation plans. The server dynamically builds an in-memory catalog by scanning markdown files in the src/ and cn/ directories, extracting problems and sections based on heading patterns. Beyond the server, the repository contains extensive educational content including markdown guides, Jupyter notebooks for ML algorithm implementations, and solution code with tests, all aimed at AI/ML interview readiness.
Peekaboo is a mature macOS desktop automation toolkit that provides a CLI, Mac app, and MCP server for screen capture, UI element detection, input synthesis, window management, and AI agent integration. The architecture is modular: a Swift CLI (peekaboo) exposes ~33 commands for interaction, system control, and AI agent orchestration; a Mac app provides a menu bar interface, session management, and visualizer overlays; a bridge daemon (PeekabooBridge) enables secure remote automation via a versioned socket protocol with operation allowlisting. The MCP server exposes native automation tools to AI clients (Claude, Cursor, etc.) via stdio transport. The agent runtime (PeekabooAgentService) supports multiple AI providers (OpenAI, Anthropic, Ollama, etc.) with streaming, tool calling, and session persistence. The codebase is written in Swift 6.2 with strict concurrency, extensive unit/integration tests, and a focus on safety (fail-closed target validation, honest effect reporting). It is not a data research project; it is a production-grade application for macOS automation.
NotebookLlaMa is a full-stack, open-source alternative to Google's NotebookLM, built on LlamaCloud and the LlamaIndex ecosystem. It provides a Streamlit-based web interface for uploading PDF documents, processing them through a multi-step workflow (parsing, extraction, summarization, Q&A generation, mind map creation), and optionally generating a podcast conversation using ElevenLabs TTS. The system uses an MCP server (FastMCP) to expose tools for document processing, mind map generation, and index querying, orchestrated by a LlamaIndex Workflow. Observability is handled via OpenTelemetry with traces exported to Jaeger and stored in PostgreSQL, with an export-to-Parquet capability. The codebase is well-structured, async-first, and includes comprehensive tests, a CLI setup wizard for embedding providers, and Docker Compose for infrastructure dependencies. It is actively maintained and demonstrates strong engineering practices.
Cog is a native macOS audio player with an exceptionally broad format support, uniquely specializing in video-game music emulation and obscure audio codec playback. Its architecture is built on a clean, modular plugin system (CogDecoder, CogSource, CogContainer protocols) that integrates dozens of vendored emulator cores (vgmstream, GME, OpenMPT, lazyusf) and general decoders (FFmpeg, TagLib). The app features a composable DSP chain for real-time effects (equalizer, HRTF binaural, tempo/pitch shifting), high-performance threading primitives (CogSemaphore), and a modern Model Context Protocol (MCP) server for remote control by AI agents. This is a classic case of a well-crafted application core coupled with a massive, critical dependency on vendored third-party code; the plugin protocols, DSP nodes, synchronization primitives, and MCP bridge are high-quality, reusable components, while the collection of emulator cores introduces significant licensing and maintenance complexity.
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.
This repository is an application: an MCP (Model Context Protocol) server paired with a Firefox browser extension that gives AI assistants like Claude Desktop read and management access to a user's real browser. The MCP server exposes nine tools over stdio — open/close/list/reorder/group tabs, search browsing history, extract a page's text and links, find/highlight text in a tab, and capture screenshots. Unlike generic web-automation MCP servers, it deliberately cannot click, modify pages, or run arbitrary scripts; page content access requires per-domain user consent and screenshots require per-tab authorization via Firefox's activeTab mechanism. Architecturally this is an Nx monorepo with three packages: mcp-server (Node 22, @modelcontextprotocol/sdk), firefox-extension (Manifest V2 WebExtension), and common (shared typed message protocol). The server communicates with the AI client over MCP stdio and with the extension over a loopback-only WebSocket (127.0.0.1 and ::1 on port 8089, or 0.0.0.0 in container mode). Every message is HMAC-SHA256 signed with a shared secret the extension generates; the extension connects outbound, so no inbound socket is exposed in the browser. Requests are matched to responses by correlation ID with timeouts, and the extension replies with resources (tabs, history, content, screenshots) over the same channel. The code is security-conscious and well-crafted: the dual-loopback binding handles IPv4/IPv6 localhost resolution differences, a capture-consent module mirrors the browser's activeTab grant lifetime to fail fast with actionable errors, the extension ships no runtime third-party dependencies, and it includes per-tool enable/disable toggles, a domain deny list, and a bounded in-browser audit log. The extension message handler has a solid Jest test suite covering consent revocation, denied domains, and disabled tools; the MCP server itself lacks unit tests. Minor debt: an unused, deprecated 'readline' npm dependency in mcp-server/package.json, an Nx version mismatch between root (22.x) and the subproject (20.x), and a few non-null assertions in the WebSocket layer. The repo is actively maintained with recent commits, a release workflow that builds DXT packages, and current dependency versions (TypeScript 5.8, Jest 30, MCP SDK 1.x, ws 8.x), so technical drift is low.