Best AI Repos & MCP Servers for Developers
Top open-source AI repositories, MCP servers, and developer tools. Curated for software engineers building with AI.
Repositories
openclaw/openclaw
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
310,753 starsollama/ollama
Ollama is an open-source platform written in Go that makes running large language models locally as straightforward as a single terminal command. Where tools like llama.cpp expose the raw inference engine, Ollama wraps the entire lifecycle -- model discovery, download, weight management, GPU acceleration, and serving -- into a polished developer experience. Running a model is as simple as typing `ollama run deepseek-v4` or `ollama run qwen3-coder`, and the system handles everything from pulling the right quantization for your hardware to allocating GPU memory and launching an API server. With over 164,000 GitHub stars and 14,700+ forks, Ollama has become the default way developers interact with open-source language models on their own machines. The project builds on llama.cpp for its inference backend but adds critical infrastructure layers on top: a model registry with thousands of pre-packaged models, automatic hardware detection across NVIDIA CUDA, AMD ROCm, and Apple Metal backends, and a REST API server that runs on localhost:11434 by default. The API is compatible with both the OpenAI Chat Completions format and, as of v0.14.0, the Anthropic Messages API -- meaning tools like Claude Code, Codex, Droid, and OpenCode can connect directly to local Ollama instances without proxy layers. The model library is one of Ollama's strongest differentiators. It provides ready-to-run versions of DeepSeek, Qwen, Gemma, Kimi-K2.5, GLM-5, MiniMax, gpt-oss, Mistral, LLaMA, Phi, and dozens more families across a range of parameter sizes and quantization levels. As of early 2026, the library supports over 40,000 model integrations. Specialized models like GLM-OCR for document understanding and Qwen3-VL for vision tasks are available alongside general-purpose chat and coding models. The `ollama launch` command, introduced in v0.15, streamlines the setup of coding agents by automatically configuring environment variables and connecting your preferred development tool to a local or cloud-hosted model. Ollama runs cross-platform on macOS, Linux, and Windows, with official Docker images for containerized deployments. Installation is a one-liner on every platform: a shell script on Linux, a DMG on macOS, or a PowerShell command on Windows. On Apple Silicon, Metal acceleration is automatic with no driver installation required -- the unified memory architecture means your full system RAM is available as GPU memory. On NVIDIA systems, CUDA drivers 535+ are detected automatically. AMD GPU support is available through ROCm 6.0+ on Linux. Recent releases have added structured output support (constraining model responses to JSON schemas), a built-in web search API, NVFP4 and FP8 quantization for up to 35 percent faster token generation on supported hardware, and a redesigned desktop application with file drag-and-drop for document reasoning. The v0.17.6 release in March 2026 refined tool calling for Qwen 3.5 models and fixed GLM-OCR prompt rendering. The project also offers cloud-hosted inference for larger models like GLM-4.6 and Qwen3-coder-480B that exceed typical consumer hardware budgets. Ollama's ecosystem integration is vast. Over 100 third-party projects connect to it, spanning web UIs (Open WebUI, LibreChat), desktop applications (AnythingLLM, Dify, Jan), orchestration frameworks (LangChain, LlamaIndex, Spring AI, Semantic Kernel), and automation platforms (n8n). Native client libraries are available in Python and JavaScript, with community libraries covering Go, Rust, Java, and more.
164,982 starslanggenius/dify
Dify is an open-source platform for building production-grade AI applications through a visual drag-and-drop workflow builder. Instead of writing boilerplate code to chain LLM calls, manage prompts, and wire up retrieval pipelines, developers lay out their logic on a canvas -- connecting model nodes, tool calls, conditional branches, and human-in-the-loop checkpoints into executable workflows. The result is a system that can go from prototype to production deployment without rewriting the orchestration layer. The platform supports hundreds of LLM providers out of the box: OpenAI GPT models, Anthropic Claude, Mistral, Llama 3, Qwen, and any provider exposing an OpenAI-compatible API. Switching between models is a dropdown change, not a code refactor. This provider-agnostic design means teams can start with a cloud API, benchmark against alternatives, and migrate to self-hosted models without touching their workflow logic. Dify ships with a full RAG pipeline built in. Upload PDFs, presentations, or plain text, and the system handles chunking, embedding, vector storage, and retrieval. Version 1.12.0 introduced Summary Index, which attaches AI-generated summaries to document chunks so semantically related content clusters together during retrieval. Version 1.13.0 added multimodal retrieval that unifies text and images into a single semantic space for vision-enabled reasoning. The agent capabilities layer supports both function-calling and ReAct-style reasoning with over 50 built-in tools including Google Search, DALL-E, Stable Diffusion, and WolframAlpha. Since v1.0.0, all models and tools have been migrated to a plugin architecture, so extending Dify with custom integrations no longer requires forking the core codebase. On the operational side, Dify includes a Prompt IDE for comparing model outputs side-by-side, LLMOps-grade logging and performance monitoring, and a Backend-as-a-Service API layer that lets frontend applications consume workflows through REST endpoints. The v1.13.0 release added a Human Input node that pauses workflows for human review, enabling approval gates and content moderation loops directly within automated pipelines. Deployment is flexible: Docker Compose for quick self-hosted setups (minimum 2 CPU cores, 4 GB RAM), Kubernetes via five community-maintained Helm charts, Terraform templates for Azure and Google Cloud, and AWS CDK for infrastructure-as-code deployments. Dify Cloud offers a managed option with 200 free GPT-4 calls to get started. Enterprise customers including Volvo Cars and Kakaku.com run Dify in production -- Kakaku.com reported 75% employee adoption with nearly 950 internal AI applications built on the platform.
132,700 starslangchain-ai/langchain
LangChain is the most widely used open-source framework for building LLM-powered applications and autonomous agents. It provides a standardized, composable interface across model providers — OpenAI, Anthropic, Google, Mistral, and 50+ others — so you can swap models without rewriting your logic. With 130,000+ GitHub stars and over 277,000 dependent projects, it's the de-facto standard for production RAG pipelines, multi-agent systems, and agentic workflows.
129,548 starsopen-webui/open-webui
Open WebUI is the most popular self-hosted AI chat platform on GitHub, with 127,000+ stars. It runs entirely offline and connects to Ollama, OpenAI-compatible APIs, and dozens of other LLM backends — giving you a ChatGPT-like experience without sending your data to any cloud.
127,076 starsanomalyco/opencode
118,000 stars in under a year. That's not a typo. OpenCode went from zero to the most-starred AI coding agent on GitHub faster than any developer tool in memory -- and it did it by being the one thing Cursor and Claude Code refuse to be: completely open source, completely provider-agnostic, and completely free. Here's the pitch that mass-converted developers: bring your own model, bring your own keys, keep your own data. OpenCode doesn't care if you're running Claude Opus, GPT-4.1, Gemini 2.5, or a local Llama instance through Ollama. It treats every provider as a first-class citizen. If you've ever felt locked into Anthropic's pricing because Claude Code only works with Claude, or locked into Cursor's $20/month because switching means losing your workflow -- OpenCode is the exit door 5 million developers already walked through. The architecture is what makes it stick. OpenCode runs a client/server split -- the AI agent runs as a background server while the TUI, desktop app, or IDE extension connects as a client. That means you can run the agent on a beefy remote machine and code from a thin laptop over SSH. Try doing that with Cursor. Two built-in agents handle different workflows: a build agent for writing and modifying code, and a read-only plan agent for exploring codebases without accidentally changing anything. There's also a general subagent that handles complex multi-step searches. LSP integration gives it real code intelligence -- not just pattern matching, but actual type-aware navigation and diagnostics. The release velocity tells its own story: 731 releases, 10,045 commits, v1.2.21 shipped March 7, 2026. The team at Anomaly (the same folks behind terminal.shop) ships daily. MCP support means you can extend it with the same server ecosystem Claude Code uses. Install takes one curl command. You're coding in 30 seconds.
121,683 starsggml-org/llama.cpp
llama.cpp is a high-performance C/C++ implementation for large language model inference, originally created by Georgi Gerganov in March 2023 shortly after Meta released its LLaMA models. With over 96,000 GitHub stars and more than 15,000 forks, it stands as one of the most consequential open-source AI projects ever built. The core premise is deceptively simple: strip away the Python runtime overhead and GPU requirements that dominate the LLM ecosystem, and rewrite inference from scratch in portable C/C++ with zero external dependencies. The project supports an extraordinary range of hardware backends -- eleven and counting -- including Apple Metal, NVIDIA CUDA, AMD HIP, Intel SYCL, Vulkan for cross-vendor GPU access, Ascend NPU via CANN, OpenCL for Adreno mobile GPUs, and experimental WebGPU for browser-based inference. This backend diversity means llama.cpp runs on everything from high-end data center GPUs to Raspberry Pis, Android phones, and iOS devices. A CPU+GPU hybrid inference mode allows models that exceed available VRAM to spill over to system RAM, making it practical to run 30B+ parameter models on consumer hardware. Quantization is where llama.cpp truly differentiates itself. The GGUF file format, which llama.cpp pioneered, supports quantization levels from 1.5-bit to 8-bit integers alongside standard float32, float16, and bfloat16 formats. Aggressive quantization (Q2_K through Q4_K) can reduce memory requirements by up to 75 percent, enabling models like LLaMA 2 13B or Mixtral 8x7B to run on machines with as little as 6-8 GB of RAM. The GGUF format has become a de facto standard, with Hugging Face providing native GGUF support and dedicated tools like GGUF-my-repo for model conversion. Beyond raw inference, llama.cpp ships with a production-ready HTTP server (llama-server) that exposes an OpenAI-compatible API. This means existing applications built against the OpenAI API can be pointed at a local llama.cpp server with minimal code changes. The server supports advanced features including speculative decoding for 1.5-2x throughput improvements on structured prompts, grammar-constrained output for reliable structured generation and function calling, and multimodal inference for vision-language models like LLaVA, MiniCPM, and Qwen2-VL. The project also includes VS Code and Vim/Neovim plugins for local code completion, a built-in web UI for interactive chat, and RPC-based distributed inference across multiple machines. Model compatibility spans over 50 text-only architectures (LLaMA, Mistral, Qwen, Phi, Gemma, Mamba, and many more) and 10+ vision-language models. Active development continues at a rapid pace, with over 8,200 commits and 758 open pull requests as of early 2026, including recent work on WebGPU shader optimization, CDNA3 tensor core flash attention for AMD MI300X GPUs, and native MXFP4 format support.
97,843 starsbrowser-use/browser-use
Browser Use is the leading open-source Python framework that enables large language models to autonomously control web browsers. With nearly 80,000 GitHub stars, it has become the de facto standard for AI-powered browser automation, allowing developers to build agents that can navigate websites, fill forms, extract data, and complete multi-step web tasks using natural language instructions. The framework takes a hybrid approach to web understanding, combining DOM parsing with vision-based analysis through Playwright. When an agent receives a task, Browser Use loads the target page, processes the DOM to identify interactive elements like buttons, links, and input fields, and optionally engages a vision model for layout-dependent decisions. The LLM then reasons about what actions to take and executes them in sequence. Browser Use is LLM-agnostic, supporting OpenAI GPT-4, Anthropic Claude, Google Gemini, and local models via Ollama, alongside its own optimized ChatBrowserUse model. The framework includes a CLI for interactive browser control, a template system for quick project scaffolding, and extensible custom actions via Python decorators. For production deployments, Browser Use offers a cloud browser infrastructure with stealth capabilities, CAPTCHA handling, and proxy rotation. Backed by a $17 million seed round led by Felicis with participation from Paul Graham and Y Combinator, Browser Use has achieved an 89.1% success rate on the WebVoyager benchmark across 586 diverse web tasks. Its MIT license and active development — with 8,739 commits and 118 releases — make it a robust foundation for any AI browser automation project.
80,692 starsinfiniflow/ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation engine that fuses deep document understanding with agentic AI capabilities to build a superior context layer for large language models. Unlike general-purpose RAG frameworks, RAGFlow specializes in extracting structured knowledge from complex, visually rich documents — including PDFs with tables, multi-column layouts, images, scanned copies, spreadsheets, slides, and web pages — with high fidelity. The platform provides template-based intelligent chunking with visual customization, high-precision hybrid search combining vector search, BM25, and custom scoring with advanced re-ranking, and grounded citations that reduce hallucinations by linking every answer back to traceable source references. RAGFlow includes a visual workflow builder for designing agentic RAG pipelines with memory support, Model Context Protocol (MCP) integration, and multi-modal model support for processing images within documents. It ships with Docker-based deployment in both lightweight (2 GB) and full-featured (9 GB) configurations, supports Elasticsearch and Infinity as storage backends, and works with configurable LLMs and embedding models. With 74,000+ GitHub stars and an Apache 2.0 license, RAGFlow has become one of the most popular open-source RAG solutions, particularly for enterprise use cases in equity research, legal analysis, and manufacturing where document intelligence is critical.
74,955 starsmem0ai/mem0
Persistent memory layer for AI agents and applications. mem0 gives LLMs the ability to remember user preferences, past conversations, and learned context across sessions — addressing the fundamental limitation that every LLM conversation starts from zero. Unlike RAG systems that retrieve static documents, mem0 continuously updates a structured memory graph as conversations happen, so the AI gets progressively smarter about each user over time.
49,875 starscrewAIInc/crewAI
Production-ready multi-agent orchestration framework that lets you define AI agents with specific roles, goals, and tools, then coordinate them to complete complex tasks through structured collaboration.
45,990 starsmsitarzewski/agency-agents
Agency-Agents is a production-ready collection of 144+ specialized AI agent personas organized across 12 divisions — Engineering, Design, Paid Media, Sales, Marketing, Product, Project Management, Testing, Support, Spatial Computing, Game Development, and Specialized. Each agent is a structured Markdown file that gives any LLM a specific professional identity, complete with domain expertise, a distinct communication style, battle-tested workflows, concrete deliverables, and measurable success metrics. Rather than relying on generic prompting, you activate a focused expert — a Frontend Wizard, Security Engineer, Brand Guardian, UX Researcher, or one of 140+ others — and the LLM narrows its context accordingly, reducing hallucinations and enforcing domain best practices. The project was born from a Reddit discussion about AI agent specialization and grew through months of community iteration into one of the fastest-starred repositories on GitHub. It integrates natively with Claude Code's /agents system by placing Markdown files in ~/.claude/agents/, and ships automated install scripts that convert agents for Cursor, Aider, Windsurf, Gemini CLI, OpenCode, and more. With 43.9K stars and 6.6K forks as of March 2026, agency-agents has become the de facto starting point for teams that want to run structured multi-agent workflows from their IDE.
43,880 starsMCP Servers
MCP Filesystem Server
The official Model Context Protocol filesystem server that gives Claude direct read and write access to files and directories on your local machine or server. Install it once and Claude can open files, create new ones, list directories, search across your codebase, and move or rename files — all without copy-pasting content into the chat window. It ships as part of the official MCP servers repository maintained by Anthropic, making it the most trusted and widely-deployed server in the ecosystem. Because you specify allowed directories at startup, Claude only ever sees what you explicitly permit — it cannot access /etc, your home folder, or any path outside the scope you configure.
81,124 starsMemory
The Memory MCP Server is an official Model Context Protocol reference server maintained by Anthropic that gives AI assistants persistent memory across conversations using a local knowledge graph. Without persistent memory, every conversation with an AI assistant starts from zero -- the model has no recollection of prior interactions, user preferences, or established context. The Memory server solves this by maintaining a structured knowledge graph stored as a local JSONL file that the assistant can read from and write to during any session. The knowledge graph is built on three core primitives. Entities are the primary nodes representing people, organizations, projects, concepts, or any other named object. Each entity has a unique name, a type classification, and a collection of observations. Relations are directed, labeled connections between entities stored in active voice (for example, "John_Smith works_at Anthropic"). Observations are discrete, atomic pieces of information attached to entities -- individual facts that can be independently added or removed without affecting other stored knowledge. The server exposes nine MCP tools that provide full CRUD operations over the knowledge graph: create_entities and create_relations for building the graph, add_observations for appending new facts to existing entities, delete_entities, delete_relations, and delete_observations for pruning outdated information, read_graph for retrieving the complete graph structure, search_nodes for querying entities by name, type, or observation content, and open_nodes for retrieving specific entities with their full context and interconnections. Storage uses a simple JSONL file format, making the knowledge base human-readable, version-controllable, and trivially portable. The storage path defaults to memory.jsonl in the working directory but can be customized via the MEMORY_FILE_PATH environment variable. Installation is straightforward through npx or Docker, and the server integrates natively with Claude Desktop, Claude Code, VS Code, Cursor, and other MCP-compatible clients. With over 44,000 weekly npm downloads and backing from the 80,000-star modelcontextprotocol/servers repository, the Memory server has become one of the most widely adopted MCP servers in the ecosystem.
81,024 starsSequential Thinking
An official MCP server that provides structured, dynamic problem-solving through sequential thinking steps. Break complex problems into manageable chunks, revise reasoning as understanding deepens, branch into alternative analysis paths, and dynamically adjust the depth of analysis — all while maintaining context across multiple steps. Part of the official Model Context Protocol servers repository maintained by Anthropic with over 80,000 GitHub stars.
81,024 starsContext7
Context7 is the most popular MCP server for injecting up-to-date, version-specific documentation and code examples directly into AI prompts. Built by Upstash, it solves the critical problem of LLMs generating broken code from outdated training data by pulling the latest official documentation from over 33,000 indexed libraries at the moment of prompting. Context7 uses an Upstash Vector Database with the DiskANN algorithm to perform semantic search across library documentation, returning relevant code snippets and API references filtered by topic. It exposes two MCP tools: resolve-library-id (which converts human-readable library names into Context7-compatible identifiers using LLM-powered search and ranking) and get-library-docs (which retrieves current documentation sections and code examples for a specific library and version). With over 44,000 GitHub stars and 240,000 weekly npm downloads, Context7 is the number one MCP server by adoption. Integration is seamless across all major AI code editors including Cursor, Claude Desktop, Windsurf, VS Code, and Claude Code. Users simply add 'use context7' to any prompt to activate real-time documentation injection, eliminating hallucinated APIs and outdated code generation.
48,933 starsChrome DevTools MCP
Ask Claude to check if your page actually renders correctly, and it opens Chrome, navigates to localhost, takes a screenshot, and tells you the hero image is 400px off-center. That's Chrome DevTools MCP — Google's official answer to the "AI coding blind" problem. Before this server existed, AI coding agents operated with a blindfold on. They could write frontend code all day, but they had zero way to verify that code actually worked in a browser. You'd paste screenshots into chat, describe layout bugs in words, copy-paste console errors — a painful feedback loop that made AI-assisted frontend work feel half-broken. Chrome DevTools MCP gives your AI agent direct access to a live Chrome browser with 29 specialized tools across six categories. Input automation (click, drag, fill forms), navigation (open tabs, wait for elements), performance profiling (record traces, analyze LCP/TBT/CLS), network inspection (catch failed API calls, CORS errors), and full debugging (evaluate scripts, read console logs with source-mapped stack traces, run Lighthouse audits). The real power shows up in compound workflows. Tell Claude to "navigate to the signup page, fill out the form, submit it, and check if the success message appears" — and it actually does it, step by step, in a real browser. Pair it with the GitHub MCP server and you get a pipeline: write code, verify it renders, commit the fix. Setup takes 60 seconds. One npx command in your MCP config and you're running. For CI/CD environments, the --headless flag runs Chrome without a visible UI. The --slim flag strips the tool count down to 3 for basic tasks when you don't need all 29. And --isolated creates temporary browser profiles that auto-clean after each session. Built on Puppeteer with automatic result waiting, so actions don't race against page loads. Works with Claude Code, Cursor, VS Code Copilot, Gemini CLI, JetBrains, and every other MCP-compatible client.
28,838 starsPlaywright MCP Server
The Playwright MCP Server is Microsoft's official Model Context Protocol server that brings full browser automation capabilities to AI assistants and LLM-powered applications. Unlike traditional screenshot-based approaches that require vision models, this server leverages Playwright's accessibility tree to provide structured, deterministic representations of web page content that any LLM can process efficiently. This fundamental design choice makes it faster, more reliable, and more token-efficient than pixel-based alternatives. The server enables AI agents to navigate websites, click elements, fill forms, extract content, take screenshots, execute JavaScript, and interact with web applications through a comprehensive set of MCP tools. It supports all major browser engines including Chromium, Firefox, WebKit, and Microsoft Edge, with built-in device emulation for over 140 mobile and tablet devices. The server automatically handles browser binary installation on first use, eliminating manual setup. Configuration is highly flexible, supporting headless and headed modes, persistent user profiles for maintaining login sessions, proxy servers, viewport customization, and storage state management. With 28,000+ GitHub stars and deep integration into GitHub Copilot's coding agent, the Playwright MCP Server has become the de facto standard for connecting AI systems to the web browser, enabling use cases from automated testing and web scraping to form filling and end-to-end workflow verification.
28,522 starsGitHub
The GitHub MCP Server is the official Model Context Protocol server maintained by GitHub, giving AI assistants and coding agents direct access to the full GitHub platform. Originally developed by Anthropic as part of the MCP reference servers collection, it was adopted and expanded by GitHub into a standalone project at github/github-mcp-server. The server exposes over 50 tools spanning repository management, issue tracking, pull request workflows, code search, notifications, code security scanning, and GitHub Actions monitoring. AI agents can create and manage repositories, browse file contents, push commits, open and merge pull requests, conduct code reviews with inline comments, search code across all of GitHub, triage issues, manage notifications, and analyze security alerts from code scanning and secret scanning. The server supports configurable toolsets via the GITHUB_TOOLSETS environment variable, allowing you to enable only the capabilities you need and reduce context window usage. It also offers a read-only mode, lockdown mode for safely working with public repositories to guard against prompt injection, and dynamic toolset discovery. Authentication uses a GitHub Personal Access Token. The server can run locally via Docker using the ghcr.io/github/github-mcp-server image, as a remote HTTP server at https://api.githubcopilot.com/mcp/ with OAuth support, or via the legacy npm package for quick local testing. It integrates natively with Claude Desktop, Claude Code, VS Code, Cursor, and JetBrains IDEs.
27,690 starscognee-mcp
The cognee MCP server connects Claude Code and other MCP clients to cognee's knowledge graph memory engine. Once connected, Claude gains persistent memory tools — it can ingest documents and code into a structured knowledge graph, query that memory using graph+vector hybrid search, analyze entire code repositories, and track developer rules across sessions. This transforms Claude from a stateless assistant into an agent with genuine persistent memory that survives across conversations.
13,885 starsFirecrawl
The Firecrawl MCP Server is the official Model Context Protocol integration from Firecrawl that brings production-grade web scraping, crawling, and structured data extraction directly into AI assistants like Claude, Cursor, Windsurf, and VS Code. Firecrawl is backed by Y Combinator and trusted by over 80,000 companies, with its core open-source project earning more than 88,000 GitHub stars. The MCP server itself has accumulated roughly 5,700 stars and receives over 8,800 weekly npm downloads, making it one of the most widely adopted web scraping servers in the MCP ecosystem. At its core, the server converts any publicly accessible website into clean, LLM-ready markdown or structured JSON by stripping ads, navigation elements, footers, cookie banners, and other boilerplate content. It handles JavaScript-rendered single-page applications, dynamically loaded content, and even PDF and DOCX documents without requiring the developer to manage headless browsers or proxy infrastructure. Firecrawl covers approximately 96 percent of the web without proxies and maintains a 95.3 percent scraping success rate with an average response time of seven seconds. The server exposes twelve MCP tools organized around five core capabilities. The scrape tool extracts content from individual pages with support for markdown, JSON, and screenshot output formats. The batch_scrape tool processes multiple URLs in parallel for high-throughput extraction workflows. The crawl tool traverses entire domains with configurable depth limits, URL filtering, and deduplication. The map tool discovers all indexed URLs on a site without requiring a sitemap. The search tool performs web searches with geographic targeting and time-based filtering, optionally scraping the full content of each result. The extract tool uses either cloud AI or self-hosted LLMs to pull structured data matching a developer-defined JSON schema. The agent tool conducts autonomous multi-step research by browsing multiple sources and synthesizing findings. Four browser tools (create, execute, delete, list) provide persistent Chrome DevTools Protocol sessions for interactive automation tasks like form filling, clicking, and scrolling. Configuration requires a single FIRECRAWL_API_KEY environment variable for cloud usage, with an optional FIRECRAWL_API_URL for self-hosted deployments. The server includes built-in resilience features: automatic retry with exponential backoff (configurable up to three attempts with delays from one to ten seconds), rate limit handling, and credit usage monitoring with configurable warning and critical thresholds. Installation takes a single command via npx, and the server supports both STDIO and Server-Sent Events transports for compatibility with remote and local MCP client configurations. A free tier provides 500 scraped pages to get started, with paid plans scaling from hobby to enterprise usage levels.
5,760 starsXcodeBuildMCP
XcodeBuildMCP is a Model Context Protocol server and CLI tool developed by Sentry (originally created by Cameron Cooke) that gives AI agents full control over Xcode for iOS and macOS development. With over 4,700 GitHub stars, it has become the go-to MCP server for Apple-platform agentic development. The server unlocks the complete developer loop directly from your AI coding assistant: build projects for simulators and real devices, run unit and UI tests, debug with LLDB breakpoints and variable inspection, take simulator screenshots, and deploy to physical devices over USB or Wi-Fi. XcodeBuildMCP operates as a per-workspace daemon that maintains state across interactions, enabling truly autonomous development workflows where your AI agent can iterate on compilation errors, fix failing tests, and verify UI changes without human intervention. It supports dual-mode operation as both an MCP server for AI clients like Claude Code, Cursor, VS Code, Windsurf, GitHub Copilot, and Xcode, and as a standalone CLI for terminal use. Project-level configuration via a YAML file allows you to specify schemes, project paths, and target simulators. The server handles code signing for device builds, supports Swift Macros, and includes Agent Skills that reduce upfront token cost by letting agents discover capabilities on demand. Requires macOS 14.5+, Xcode 16+, and Node.js 18+ (Node not needed for Homebrew installs).
4,719 starsAtlassian MCP Server
An MCP server that connects AI assistants to Jira and Confluence — the two most widely-used project management and knowledge management tools in enterprise software. Ask your AI to search issues, create tickets, update sprint status, query Confluence pages, and manage your entire Atlassian workspace through natural language.
4,608 starsNotion MCP Server
Official first-party MCP server from Notion Labs that gives AI assistants full read and write access to Notion workspaces — create pages, query databases, manage comments, and search across connected tools including Slack, Google Drive, and Jira.
4,038 stars