Enables interaction with AIApp BaaS authentication system through keyword-based document search and automatic generation of framework-specific client code. Supports React, Next.js, Vue, and Vanilla JS with TypeScript integration and automatic project ID injection.
A sample implementation of a Model Context Protocol server using Next.js and the Vercel MCP Adapter, allowing developers to create AI assistants with custom tools and resources.
Provides a trust layer for agent-to-agent commerce and delegated agent authority, allowing MCP-capable agents to install and fetch the latest Internet Court skill dynamically.
A Next.js-based MCP server template that implements OAuth 2.1 authentication with support for both SSE and Streamable HTTP transports. Provides a reference implementation for building authenticated MCP servers that work with Claude, Cursor, VSCode, and other MCP clients.
A drop-in MCP server implementation for Next.js projects using Vercel MCP Adapter, allowing developers to integrate model context protocol functionality with custom tools, prompts, and resources.
A Node.js MCP server that loads plugins, registers tools via the MCP SDK, and exposes functionality over stdio with built-in persistence and a Next.js web UI. It provides a management stack for MCP tools with plugin lifecycle management and security controls.
Automatically generates typed React hooks for Next.js projects by crawling API routes, GraphQL queries, and components. Analyzes pages to suggest optimal render modes (SSR/CSR/ISR) and produces documentation with performance guidance.
An MCP server that provides persistent memory for AI assistants by automatically recording and injecting project-specific rules, decisions, and configurations through session hooks. It utilizes Gemini to analyze conversations, manage token-efficient context, and perform autonomous background tasks like documentation updates.
Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.
Provides Claude Code with programmatic session awareness to track context usage, session history, and task progress. It enables intelligent context reset recommendations and automatic synchronization of project planning documentation.
An intelligent tool that automates the setup of new Model Context Protocol (MCP) server projects through a conversational interface. It generates project structures, technical specifications, and context-rich documentation to streamline AI-assisted development in TypeScript or Python.
A production-ready FastMCP server template with modular architecture for building MCP servers with organized tools, resources, and prompts, featuring container support and AI agent documentation system.
A server that provides both comprehensive API documentation and direct robot control capabilities for Opentrons Flex and OT-2 robots, enabling users to manage protocols, control runs, and monitor robot health through natural language.
Enables AI agents to interact with the Godot game engine, including project inspection, scene/script parsing, headless exports, runtime control with live scene-tree inspection and evaluation, and API documentation search.
Generates project specifications and file structures using Claude AI through MCP integration. Enables users to describe project ideas and automatically create corresponding documentation and project files.
An MCP server that allows AI agents to manage BuildBase resources (users, workspaces, subscriptions, projects) via 48 tools, with built-in agent authentication and discovery.
Bridges Ghidra's reverse engineering capabilities with AI tools through 179 specialized tools for automated binary analysis and documentation. It supports full read/write access for function decompilation, renaming, and cross-binary documentation transfer in both GUI and headless modes.
A Model Context Protocol (MCP) server that implements AI-First Development framework principles, allowing LLMs to interact with context-first documentation tools and workflows for preserving knowledge and intent alongside code.