"Resources and Techniques for Testing in React" matching MCP servers:

MCP server for the ReactUse library. Exposes 110+ React Hook signatures, examples, and API metadata so AI coding assistants can discover and use any hook from @reactuses/core directly.

A production-grade TypeScript starter for building Model Context Protocol servers, supporting stdio and Streamable HTTP transports with modular tools, resources, and prompts.

A small, readable MCP learning project implemented in TypeScript that uses a local JSON notebook to demonstrate core MCP features such as tools, resources, prompts, sampling, and notifications.

Provides access to PatternFly React documentation, development rules, and component schemas through MCP tools, enabling AI assistants to help developers build applications with PatternFly components following best practices.

A production-grade, extensible Python template for building Model Context Protocol servers with support for Streamable HTTP and stdio transports. It provides a structured framework for implementing tools, resources, and prompts with built-in authentication, observability, and background task management.

Provides complete access to Model Context Protocol documentation through an MCP server, allowing LLMs to query specific sections like 'Tools', 'Resources', and 'Authorization' to retrieve comprehensive protocol specifications.

Habitica MCP server built with Effect v4, currently exposing a hello-world tool, resource, and prompt over stdio for early development and testing.

Exposes GOV.UK Frontend components and Design System patterns and styles as MCP resources for use with AI assistants.

MCP server providing token-efficient access to OpenAPI/Swagger specs via MCP Resources for client-side exploration.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

Enables multi-level reasoning with configurable depth levels, session-based state management, structured thought input, and real-time trace resources.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Local MCP server for generating image assets with Google Gemini (Nano Banana 2 / Pro). Supports transparent PNG/WebP output, exact resizing/cropping, up to 14 reference images, and Google Search grounding.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.

MCP server for Fizzy kanban task management with tools for boards, cards, comments, and checklists.

Unofficial MCP server for the messdienst24.de utility portal, providing tools to retrieve heating and hot-water consumption data, download PDF reports, and compare usage trends.

Enables AI assistants to search live jobs, view full details, save and track applications in a Kanban board, set follow-up reminders, and subscribe to job alerts—no account needed for searching.

Standalone MCP server for Obsidian vaults - hybrid search (FTS5 + vector + cross-encoder reranking), images and PDFs in agent-readable form, Kanban-aware tasks (Tasks-plugin + Dataview formats), structured memory with topic recall, fine-grained read/write tools for optimal token efficiency, and link graph support. Run locally, self-host, or one-click deploy for remote access. OAuth 2.1.

MCP server for WeChat Mini Program debugging and automation, enabling agents to perform UI operations, screenshots, and regression testing through natural language commands.