"Latest Next.js Documentation" matching MCP servers:

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 atomic key-value store for AI agent coordination, ensuring agents always work with the latest state.

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.

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 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.

Enables AI assistants to save and retrieve code snippets, generate language-specific style guides, create wiki documentation, and orchestrate multi-agent documentation workflows through MCP tools backed by vector search.

Enables AI assistants to discover and invoke MCP tools for saving and retrieving code snippets, performing semantic vector search, generating language-specific style guides, creating wiki documentation, and orchestrating multi-agent documentation workflows.

Enables AI assistants to save, retrieve, and semantically search code snippets, generate language-specific style guides, create wiki documentation, and orchestrate multi-agent documentation workflows through MCP tools.

Enables AI assistants to save and retrieve code snippets, perform semantic code search, generate language-specific style guides and deep wiki documentation, and orchestrate multi-agent workflows for comprehensive documentation.

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.

Lets parallel DSH agent sessions on the same machine discover what each other is doing — which workspace, which tool is running, the latest human request and reply — so they can spot overlapping file edits and git races, and leave notes for one another on a shared append-only board.

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.

Enables long-running coding or documentation tasks to save incremental checkpoints, resume after interruptions, restore any previous step, and hand off work across sessions or machines using only the Python standard library.

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.