Figma MCP Server Best Practices: From Local Dev to Team Collaboration
A comprehensive guide to best practices for the Figma MCP Server. Learn how to optimize your local and remote setups for a seamless design-to-code workflow.
by stripe
Provides SDKs and tools to integrate Stripe's billing and API services with large language models, agent frameworks, and token‑metering for AI‑powered products and businesses.
by MiniMax-AI
Enables interaction with powerful text‑to‑speech, image generation and video generation APIs through a Model Context Protocol server.
by redis
Offers a natural language interface that lets AI agents store, retrieve, and search data in Redis, handling strings, hashes, lists, sets, sorted sets, streams, JSON, and vector indexes.
by qdrant
Provides a Model Context Protocol server that stores and retrieves semantic memories using Qdrant vector search, acting as a semantic memory layer.
by perplexityai
Enables Claude and other MCP‑compatible applications to perform real‑time web searches through the Perplexity (Sonar) API without leaving the MCP ecosystem.
by mindsdb
Enables humans, AI agents, and applications to retrieve highly accurate answers across large‑scale data sources, unifying heterogeneous databases, warehouses, and SaaS platforms.
by IBM
A central management point that unifies REST, MCP, and streamable HTTP endpoints, providing federated discovery, authentication, rate‑limiting, observability, virtual servers, and an optional admin UI for AI tools, prompts, and resources.
by ComposioHQ
Connect AI chat tools to 500+ business and productivity apps, enabling plain‑English commands to trigger actions like sending emails, creating tasks, updating records, and more.
by netdata
Delivers real‑time, per‑second infrastructure monitoring with zero‑configuration agents, on‑edge machine‑learning anomaly detection, and built‑in dashboards.
by modelcontextprotocol
A Model Context Protocol server for Git repository interaction and automation.
by modelcontextprotocol
An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.
by upstash
Provides up-to-date, version‑specific library documentation and code examples directly inside LLM prompts, eliminating outdated information and hallucinated APIs.
by mindsdb
Enables humans, AI agents, and applications to retrieve highly accurate answers across large‑scale data sources, unifying heterogeneous databases, warehouses, and SaaS platforms.
by daytonaio
Provides a secure, elastic infrastructure that creates isolated sandboxes for running AI‑generated code with sub‑90 ms startup, unlimited persistence, and OCI/Docker compatibility.
by github
Connects AI tools directly to GitHub, enabling natural‑language interactions for repository browsing, issue and pull‑request management, CI/CD monitoring, code‑security analysis, and team collaboration.
by microsoft
Provides fast, lightweight browser automation using Playwright's accessibility tree, enabling LLMs to interact with web pages through structured snapshots instead of screenshots.
by zed-industries
A high‑performance, multiplayer code editor designed for speed and collaboration.
by cline
An autonomous coding assistant that can create and edit files, execute terminal commands, and interact with a browser directly from your IDE, operating step‑by‑step with explicit user permission.
by danny-avila
Provides a self‑hosted ChatGPT‑style interface supporting numerous AI models, agents, code interpreter, image generation, multimodal interactions, and secure multi‑user authentication.
by RooCodeInc
Provides an autonomous AI coding partner inside the editor that can understand natural language, manipulate files, run commands, browse the web, and be customized via modes and instructions.
A desktop‑optimized AI chatbot that connects to any LLM, supports multi‑modal inputs (audio, PDF, images, text files), provides real‑time web search via Tavily or a local browser, and keeps all user data stored locally for privacy.
AI‑powered autonomous coding agents, context‑aware autocomplete, terminal command automation, and deep IDE integrations accelerate software development and streamline codebase navigation.
Provides a native macOS app that consolidates multiple AI models for chat, code assistance, content creation, and image generation directly inside everyday applications.
AI-powered code assistance directly inside the terminal, providing instant codebase search, coordinated multi‑file edits, test execution, and seamless integration with IDEs, CI/CD pipelines, and common developer tools.
Provides an AI conversational assistant accessible via desktop and mobile applications, allowing users to continue conversations across devices without switching tabs.
An AI‑powered code editor that boosts developer productivity with intelligent autocompletion, natural‑language code editing, and deep codebase awareness.
AI-powered code editing with contextual chat, next‑edit suggestions, and agent mode, supporting dozens of languages and deep UI customization.
Provides an integrated, customizable AI coding agent that automates routine development tasks, writes, tests, and deploys code, and can be extended with custom Zen Agents for any workflow.
by wheattoast11
Orchestrates a network of asynchronous agents and streaming swarms to conduct ensemble‑consensus research, automatically creating indexed PGlite databases in WebAssembly and providing semantic, hybrid, and SQL search capabilities.
by Michael-Obele
Provides real-time access to shadcn-svelte component documentation and developer utilities via web scraping, exposed through an MCP server with HTTP and SSE transports.
by BingoWon
Provides AI agents instant access to Apple developer documentation through semantic, keyword, and hybrid retrieval‑augmented generation (RAG) techniques.
by juspay
A unified AI development platform that provides a TypeScript SDK and professional CLI to build, test, and deploy applications across multiple AI providers, with built‑in tools, MCP integration, and enterprise‑grade features such as Redis conversation memory, cost optimization, and multi‑provider failover.
by toolsdk-ai
Provides a unified enterprise gateway and registry for discovering, securing, and executing Model Context Protocol (MCP) tools via a standardized HTTP API with sandboxing and OAuth 2.1 support.
by microcmsio
Enables AI assistants such as Claude to retrieve, create, review, and upload content in microCMS via a Model Context Protocol server.
by ntk148v
Provides programmatic access for AI assistants to query and manage Prometheus Alertmanager resources, supporting authentication, multi‑tenant handling, and pagination to fit LLM context limits.
by aashari
Provides a production‑ready foundation for building custom Model Context Protocol servers in TypeScript, featuring IP geolocation tools, a full CLI, dual STDIO/HTTP transports, TOON output format, JMESPath filtering, and an extensible 5‑layer architecture.
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A comprehensive guide to best practices for the Figma MCP Server. Learn how to optimize your local and remote setups for a seamless design-to-code workflow.
Step-by-step guide to installing your first MCP server. From setup to integration with clients like Claude Desktop, Cursor, and VSCode, get your AI assistant connected to external tools quickly and securely.
Learn how to create custom MCP servers that integrate with any AI assistant. Complete with code examples, best practices, and deployment strategies for your specialized use cases.
Everything you need to know about MCP Server Space and MCP servers.
The Model Context Protocol (MCP) is an open standard that enables AI models like Claude to securely connect with external data sources, tools, and services. MCP servers expose capabilities that AI assistants can use to enhance their functionality.
The Model Context Protocol (MCP) was introduced by Anthropic and open-sourced. It is a community-driven standard designed to foster an open ecosystem for AI model integration.
Listings include permissions, capabilities, and source links. We run basic checks, but we don’t guarantee security—always review code and sandbox servers.
MCP securely extends AI capabilities by connecting to external data and tools. Common use cases: real-time Q&A, automated workflows, and enterprise knowledge integration.
Add the server to your MCP-compatible client configuration (for example, a “mcpServers” section). Follow the server’s README for auth and environment variables.
Yes. SDKs are available in multiple languages (commonly TypeScript and Python). Start with examples and guidelines, then publish your server to the community.
Popular clients include those by Anthropic and community projects. Check your client’s documentation for MCP support and configuration format.
Submit your server with a repo link, brief capabilities, and permission notes. We’ll review basic details before publishing.