"A server for exploring design concepts or resources" matching MCP servers:

Enables coding agents to read ground truth about a React design system — components, props, cva variants, tokens and docs — over MCP so they write UI that matches it. It also exposes a check_ui linter agents run on their own output, returning rule ids, locations and concrete fixes before review.

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

A task-board MCP server that provides tools to search, create, and update tasks, resources for reading the board, and prompts for standup and triage workflows.

Provides a Model Context Protocol server with tools for knowledge base search, retrieval, and addition, plus system time, resources, and prompts. Supports stdio and Streamable HTTP transports for local and shared deployments.

Provides expert design and marketing guidance for AI coding agents through curated knowledge documents and tools. Covers UI/UX, copywriting, SEO, GEO, and conversion optimization.

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

Enables MCP-capable IDEs to search 8,214 design-system components, retrieve exact source bundles and semantic tokens, and follow a copy-first handoff workflow with composition validation.

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.

A Model Context Protocol server for interacting with MongoDB Databases and MongoDB Atlas. It enables AI assistants to query, manage, and operate on MongoDB data and Atlas resources through natural language.

A Model Context Protocol server that enables AI assistants to interact with MongoDB Atlas resources through natural language, supporting database operations and Atlas management functions.

A minimal TypeScript starter template for building Model Context Protocol (MCP) servers with auto-loading architecture for tools, resources, and prompts. Includes code generators, dual transport support (stdio/HTTP), and production-ready structure.

Enables AI assistants to interact with KiCAD for PCB design automation, including schematic editing, component placement, routing, DRC/ERC, and export.

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.

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.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

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

Deterministic encode, decode, hash and convert for agents, on the same pipeline engine as the String Utility Belt web app, CLI and editor extensions: a pipeline built and checked in the browser runs unchanged from its share link. Base64/hex/URL, JWT, hashes, JSON/YAML/CSV/XML, dates and regex; tools to search, describe and run utilities or whole pipelines, and to detect input formats. Runs locally

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.