Featured MCP Servers

Delivers real‑time, per‑second infrastructure monitoring with zero‑configuration agents, on‑edge machine‑learning anomaly detection, and built‑in dashboards.

Compress tool outputs, logs, files, RAG chunks, and conversation history before they reach the LLM, keeping answers identical while saving up to 95% of tokens for JSON payloads.

A Model Context Protocol server for Git repository interaction and automation.

An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.

Provides an open-source API layer that enables local OpenAI‑compatible models to be used for production AI applications, offering standardized message handling, document ingestion, retrieval‑augmented generation, tool integration, and MCP connectivity.

Provides up-to-date, version‑specific library documentation and code examples directly inside LLM prompts, eliminating outdated information and hallucinated APIs.

Enables humans, AI agents, and applications to retrieve highly accurate answers across large‑scale data sources, unifying heterogeneous databases, warehouses, and SaaS platforms.

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.

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.

Provides fast, lightweight browser automation using Playwright's accessibility tree, enabling LLMs to interact with web pages through structured snapshots instead of screenshots.

A self‑hosted web application firewall and reverse proxy that protects web applications from attacks and exploits by filtering, monitoring, and blocking malicious HTTP/S traffic.

A self‑hosted, open‑source platform that provides a no‑code builder for creating, versioning, and running AI‑driven automation workflows. Pieces are TypeScript‑based plugins that become MCP servers, allowing direct consumption by large language models.

Automates browser‑based workflows by leveraging large language models and computer‑vision techniques, turning natural‑language prompts into fully functional web interactions without writing custom scripts.

Build, debug, and scale LLM workflows with a high‑performance C++ core and over 50 extensible Python nodes, supporting multiple model providers, vector databases, and agent orchestration, all from within your IDE.

Provides IDE‑like semantic code retrieval and editing tools that turn any LLM into a powerful coding assistant operating directly on your codebase, without being tied to a specific model or interface.

Runs Android emulators inside Docker containers with VNC access, log sharing, adb connectivity, video recording and optional MCP server support.

Provides a Model Context Protocol server that enables coding agents to control and inspect a live Chrome browser through Chrome DevTools, supporting reliable automation, deep debugging, and performance analysis.

Enables Claude AI to control Blender for prompt‑assisted 3D modeling, scene creation, and manipulation via a socket‑based Model Context Protocol server.

Provides TypeScript libraries to build Model Context Protocol servers and clients, supporting Node.js, Bun, and Deno.

Create, edit, and render spreadsheets, documents, and presentations on web and server, supporting AI-driven natural language interaction.

An MCP server that streamlines database tool development by handling connection pooling, authentication, observability, and secure access, allowing agents to interact with databases via natural language.

Track workouts, plan routines, log sets, and monitor body‑weight progress entirely on your own server, with offline‑first PWA support and secure passkey authentication.

Connect APIs quickly with a free, hosted integration platform that enables event‑driven automations across 1,000+ services and supports custom code in Node.js, Python, Go, or Bash.

Orchestrates Claude Code, Gemini CLI, Codex CLI and a wide range of AI models to provide continuous, context‑rich workflows for code analysis, debugging, planning and pre‑commit validation.

Organizes email inbox, drafts replies in the user's tone, tracks follow‑ups, and provides analytics to achieve inbox zero quickly.

A self‑hosted, serverless CMS platform built on AWS that enables developers to extend content management with TypeScript code and AI‑assisted tooling.

A comprehensive, 430K‑line reference that teaches the why behind Claude Code, covering agentic workflows, hooks, skills, MCP servers, quizzes, security hardening, and production‑ready templates.

Turns vague development ideas into verified, runnable codebases by orchestrating interview, specification, execution, and evaluation loops across multiple LLM runtimes.

Manages and switches provider configurations and MCP servers for Claude Code and Codex on Windows, macOS, and Linux, supporting custom endpoints, cloud‑synced directories, and rapid configuration import/export.

Provides web scraping, crawling, search, and structured data extraction for LLM clients through a Model Context Protocol server.

Provides an AI‑powered toolkit for instant localization of web and mobile applications using LLMs, supporting build‑time compilation, CLI commands, CI/CD automation, and real‑time SDK translation.

Command line interface for managing, testing, and deploying Firebase projects, offering utilities for hosting, cloud functions, realtime database, Firestore, authentication, extensions, and local emulation.