Skybridge is a production-grade, full-stack TypeScript framework for building interactive MCP (Model Context Protocol) Apps and ChatGPT Apps. It provides a unified runtime to develop server-side tools with rich React-based UI views that render inside AI chat hosts like ChatGPT, Claude, Cursor, and VSCode. The framework is structured as a monorepo with packages for the core server/client library (`skybridge`), a Vite plugin for build orchestration, a developer tools SPA for local debugging, a project scaffolding CLI (`create-skybridge`), and an eval/test harness. The architecture centers on a typed MCP server implementation that handles tool registration, per-tool OAuth 2.1 authentication, an onion-model middleware system, and view resource serving. On the client side, a bridge abstraction (HostAdaptor) normalizes communication across different host protocols (OpenAI Apps SDK vs. MCP App JSON-RPC), while React hooks provide type-safe access to tool calls, view state, user context, and display modes. The framework emphasizes a 'reason, then render' two-phase pattern: a tool call computes data, then a view displays it interactively. Extensive examples cover e-commerce, games, authentication, productivity dashboards, and generative UI, demonstrating the framework's flexibility. Skybridge is not a data research project; it is a developer toolchain for building agent-native applications.
This repository implements an MCP (Model Context Protocol) server that exposes IDA Pro's disassembler, decompiler, debugger, and type system as a set of tools and resources consumable by LLM clients (Claude, Codex, Cursor, etc.). It operates in two distinct modes: an IDA GUI plugin that starts an MCP server on a configurable port, and a headless supervisor (`idalib-mcp`) that manages persistent worker processes for multiple databases. The architecture is a hybrid of a plugin and a standalone server, built on a custom, vendored MCP server implementation (`zeromcp`) with HTTP/SSE/stdio transports. The core API surface is defined via a decorator-based registration system (`@tool`, `@resource`, `@unsafe`, `@ext`) in `rpc.py`, which also handles output truncation middleware and JSON schema generation from TypedDicts. All IDA SDK calls are marshalled onto the main thread through a robust execution pipeline (`sync.py`) that manages timeouts, cancellation, and batch mode. The headless mode uses a supervisor/worker pattern (`idalib_supervisor.py`, `idalib_server.py`, `worker_lifecycle.py`) with health probing, idle TTL, and session management. A comprehensive compatibility layer (`compat.py`) provides version-gated shims for IDA 8.4 through 9.3. Key features include decompilation, disassembly, cross-references, memory read/write, patching, type declaration/inference, debugger control (breakpoints, step, registers), signature generation, and binary survey. Tool calls are always-on traced into the IDB as gzipped JSONL batches. The project includes an extensive test suite with a custom test framework, MCP spec regression tests, and CI running against multiple IDA versions in Docker containers. The codebase is actively maintained and demonstrates strong engineering practices, but the plugin mode is explicitly deprecated in favor of the official Hex-Rays IDA MCP Server. The heavy reliance on IDA's internal APIs and the vendored MCP server implementation represent ongoing maintenance risks. The code is well-structured with several genuinely reusable components, though some modules (e.g., `api_analysis.py`) are monolithic.
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.
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 is a VS Code extension that provides a unified interface for discovering, installing, and managing MCP (Model Context Protocol) servers and AI agent skills. It integrates with multiple sources: the Official MCP Registry, skills.sh, GitHub repositories, and CloudMCP (a hosted alternative). The extension offers a chat participant (`@mcp`) for natural-language search and install, and a sidebar webview with search and management tabs. It supports 30+ AI coding agents including GitHub Copilot, Claude Code, and Codex CLI. The architecture consists of a TypeScript extension backend (using esbuild for bundling) and a React 19 + Vite + Tailwind CSS + shadcn/ui frontend, communicating via typed RPC messages. Telemetry is collected anonymously with standardized error classification. The codebase is well-structured with clear separation of concerns, CI/CD pipelines, and unit tests.
Docfork is a documentation lookup service designed specifically for AI coding agents and developers. It provides up-to-date, version-pinned documentation through a Model Context Protocol (MCP) server with two core tools: `search_docs` for finding relevant documentation sections and `fetch_doc` for retrieving full content. The service is accessed via IDE plugins (Cursor, Claude Code, etc.), a command-line interface (dgrep), and a TypeScript SDK. The architecture features a monorepo structure with separate packages for the MCP server, CLI tool, SDK, and IDE integrations, using Turborepo for build orchestration and pnpm workspaces for package management. Key features include authentication via API keys or OAuth, team-based 'Cabinets' to scope documentation to specific project stacks, and privacy-first telemetry that collects only metadata without querying user data.