Top MCPs by Category

Chrome MCP Server is a Chrome extension-based Model Context Protocol (MCP) server that exposes your Chrome browser functionality to AI assistants like Claude, enabling complex browser automation, content analysis, and semantic search. It leverages your existing Chrome environment, including login states and configurations, to allow large language models and chatbots to control the browser natively without needing to launch a separate automation process. The project emphasizes privacy by remaining fully local and offers capabilities such as cross-tab context, streamable HTTP communication, and a built-in vector database for semantic search and content analysis. As an early-stage project, it includes a growing set of tools for browser control, inspection, and automation, with ongoing development to broaden compatibility and features.

Claude Skills MCP Server is an MCP server that enables intelligent search and retrieval of Claude Agent Skills using vector embeddings and semantic similarity. It implements a progressive disclosure architecture so AI applications can discover and load skills in stages (metadata → full content → files) while remaining fast and local. The server can load skills from multiple sources, including Official Anthropic Skills, K-Dense AI Scientific Skills, and local directories, providing a zero-configuration experience out of the box for Cursor or standalone usage. The architecture is split into a lightweight frontend and a heavy backend, enabling instant startup and background backend download, with no API keys required and the ability to connect to remote hosted backends if desired.

Appwrite MCP server is a Model Context Protocol server that enables AI models to interact with Appwrite’s backend. It provides a curated set of MCP tools to manage databases, users, functions, teams, and more within your Appwrite project, enabling powerful AI-assisted workflows and natural-language interactions with your backend. The server ships with the Databases tools enabled by default to keep prompts within context limits and can be extended by enabling additional APIs via command-line flags. This makes it easier to build AI-powered applications that leverage Appwrite APIs securely and efficiently.

MCP Server Templates (Legacy) is a flexible platform that provides Docker and Kubernetes backends, a lightweight CLI (mcpt), and client utilities for seamless MCP integration. It enables you to spin up servers from templates, route requests through a single endpoint with load balancing, and support both deployed (HTTP) and local (stdio) transports — all with sensible defaults and YAML-based configs. This legacy variant lays the groundwork for MCP integrations, while offering a clear upgrade path to the newer MCP Platform. The project emphasizes migration guidance to keep existing configurations working as you move to enhanced architecture and capabilities.

The plugged.in MCP Proxy Server operates as a central hub that aggregates multiple Model Context Protocol (MCP) servers into a single, unified interface. It orchestrates knowledge, memory, and tools across connected MCPs, enabling clients to query documents, manage memory, and invoke tools from various servers through one connection. With support for STDIO, Server-Sent Events (SSE), and Streamable HTTP transports, it enables seamless integration with popular MCP clients like Claude Desktop, Cline, and Cursor while providing policy, telemetry, and registry features for scalable deployments. This proxy fetches tool, prompt, and resource configurations from the plugged.in App APIs and exposes a unified catalog of capabilities. It supports static built-in tools, memory clipboard operations, and dynamic tools discovered from connected MCP servers, including tool discovery, RAG-based search, document management, and notifications. The hub also offers configuration options for HTTP transport, authentication, and session management, making it possible to run as a stateless HTTP service or a stateful STDIO proxy, with optional API-key protection for HTTP endpoints.

Magg is an MCP Aggregator – a meta-MCP server that manages, aggregates, and proxies multiple MCP servers. It acts as a central hub for discovering, configuring, and orchestrating MCP servers, allowing large language models to extend their capabilities at runtime. Magg exposes a suite of tools to search, add, configure, enable/disable, and proxy MCP servers and their tools, merging them under unified prefixes and persisting configurations across sessions. It also includes built-in health and status tools, Real-time Notifications, and MBro (MCP Browser) for interactive exploration, making it easier to compose, manage, and monitor complex MCP ecosystems. Whether you’re running stdio, HTTP, or hybrid transports, Magg provides flexible deployment modes, kit management, and secure access with optional JWT-based authentication.

The plugged.in MCP Proxy Server operates as a central hub that aggregates multiple Model Context Protocol (MCP) servers into a single, unified interface. It orchestrates knowledge, memory, and tools across connected MCPs, enabling clients to query documents, manage memory, and invoke tools from various servers through one connection. With support for STDIO, Server-Sent Events (SSE), and Streamable HTTP transports, it enables seamless integration with popular MCP clients like Claude Desktop, Cline, and Cursor while providing policy, telemetry, and registry features for scalable deployments. This proxy fetches tool, prompt, and resource configurations from the plugged.in App APIs and exposes a unified catalog of capabilities. It supports static built-in tools, memory clipboard operations, and dynamic tools discovered from connected MCP servers, including tool discovery, RAG-based search, document management, and notifications. The hub also offers configuration options for HTTP transport, authentication, and session management, making it possible to run as a stateless HTTP service or a stateful STDIO proxy, with optional API-key protection for HTTP endpoints.

MCP Access Point is a lightweight gateway that turns existing HTTP services into MCP (Model Context Protocol) endpoints with zero code changes. Built on high-performance Pingora proxy, it enables seamless protocol conversion between HTTP and MCP, supporting both SSE and Streamable HTTP. Designed for multi-tenant deployments, it offers a RESTful Admin API for real-time configuration management, dynamic updates, and resource administration without restarting the service. This repository provides a clear Quick Start, multi-tenancy guidance, and admin operations to manage upstreams, services, routes, and more, making it easy to expose legacy HTTP APIs to MCP clients like Cursor Desktop and MCP Inspectors.

A Model Context Protocol (MCP) tool server designed for OpenAI's GPT-4o and gpt-image-1 image generation and editing APIs. This MCP server exposes image-generation capabilities via two primary tools, create-image and edit-image, enabling developers to generate images from prompts and perform inpainting, outpainting, or compositing edits with fine-grained prompt control. It also provides file-output options so generated content can be saved to disk or returned as base64, and it supports a range of MCP-compatible clients, including Claude Desktop, Cursor, VSCode, Windsurf, among others. Built on the MCP SDK and OpenAI and OpenAI-compatible tooling, this server offers a ready-to-run solution for integrating image APIs into MCP-enabled workflows.

Imagen3-MCP is an image generation service based on Google's Imagen 3.0 that exposes its functionality through MCP (Model Control Protocol). The project provides a server to run a local MCP service that accesses Google Gemini-powered image generation, enabling developers to integrate advanced image synthesis into their applications. The documentation covers prerequisites (Gemini API key), installation steps for Cherry Studio, and a Cursor-based JSON configuration example for embedding the MCP server in broader tooling. This MCP is designed to be deployment-friendly, with configurable environment variables and optional proxy settings to adapt to various network environments.

MCP Bundles Hub MCP Server provides direct, unified access to tools from all your enabled MCP bundles through a single authenticated endpoint. It enables executing tools, discovering what tools are available across bundles, searching by name, provider, or capability, and checking readiness and details for each tool. This hub server consolidates bundle tools into one MCP interface, streamlining AI-assisted workflows by securely managing credentials and ensuring bundle-aware execution. Built with OAuth and API key authentication, it supports tool discovery, readiness checks, and detailed tool information, making it easier to scale tool access across multiple providers and bundles.

MindsDB ships with a built-in MCP (Model Context Protocol) server that enables MCP applications to connect, unify and respond to questions over large-scale federated data. It spans databases, data warehouses, and SaaS applications, allowing you to query across diverse sources in a unified manner. The MCP server is integrated into MindsDB's architecture with the Connect-Unify-Respond philosophy, and you can learn more about MCP at docs.mindsdb.com/mcp/overview. You can install MindsDB Server via Docker Desktop or Docker, and the MCP server is part of the standard MindsDB deployment.

MCP Bundles Hub MCP Server provides direct, unified access to tools from all your enabled MCP bundles through a single authenticated endpoint. It enables executing tools, discovering what tools are available across bundles, searching by name, provider, or capability, and checking readiness and details for each tool. This hub server consolidates bundle tools into one MCP interface, streamlining AI-assisted workflows by securely managing credentials and ensuring bundle-aware execution. Built with OAuth and API key authentication, it supports tool discovery, readiness checks, and detailed tool information, making it easier to scale tool access across multiple providers and bundles.

Pipedream MCP Server is a reference implementation for self-hosting a Model Context Protocol (MCP) server. It showcases how to manage and serve MCP-based apps and tools in your own environment, providing you with a way to run MCP servers locally or within your organization. Note that this MCP server is a reference implementation and is no longer actively maintained; for production workloads, Pipedream recommends using the remote MCP server, which offers hosted reliability and scaling. The server supports two primary modes and integrates with Pipedream Connect for authentication and API management, enabling automatic app discovery and credential storage with enterprise-grade security.

Magg is an MCP Aggregator – a meta-MCP server that manages, aggregates, and proxies multiple MCP servers. It acts as a central hub for discovering, configuring, and orchestrating MCP servers, allowing large language models to extend their capabilities at runtime. Magg exposes a suite of tools to search, add, configure, enable/disable, and proxy MCP servers and their tools, merging them under unified prefixes and persisting configurations across sessions. It also includes built-in health and status tools, Real-time Notifications, and MBro (MCP Browser) for interactive exploration, making it easier to compose, manage, and monitor complex MCP ecosystems. Whether you’re running stdio, HTTP, or hybrid transports, Magg provides flexible deployment modes, kit management, and secure access with optional JWT-based authentication.

MetaMCP is a MCP proxy that lets you dynamically aggregate MCP servers into a unified MCP server, and apply middlewares. MetaMCP itself is a MCP server so it can be easily plugged into ANY MCP clients. It functions as an MCP Aggregator, Orchestrator, Middleware, and Gateway all in one docker image, enabling scalable, configurable hosting of multiple MCP servers behind a single endpoint with flexible authentication, tooling, and annotations. This README introduces core concepts such as MCP Server configurations, Namespaces, Endpoints, Middleware, Inspector, and Tool Overrides & Annotations, and provides quick-start guidance for running MetaMCP with Docker, building a development environment, and integrating with clients like Claude Desktop via proxies. It also covers MCP protocol compatibility, authentication options (including API keys, OAuth, and OIDC), and integration guidance for developers looking to remix MCP tool flows and middleware pipelines.

Magg is an MCP Aggregator – a meta-MCP server that manages, aggregates, and proxies multiple MCP servers. It acts as a central hub for discovering, configuring, and orchestrating MCP servers, allowing large language models to extend their capabilities at runtime. Magg exposes a suite of tools to search, add, configure, enable/disable, and proxy MCP servers and their tools, merging them under unified prefixes and persisting configurations across sessions. It also includes built-in health and status tools, Real-time Notifications, and MBro (MCP Browser) for interactive exploration, making it easier to compose, manage, and monitor complex MCP ecosystems. Whether you’re running stdio, HTTP, or hybrid transports, Magg provides flexible deployment modes, kit management, and secure access with optional JWT-based authentication.

MCPJungle is a self-hosted MCP Gateway and Registry for AI agents. It serves as a central registry and gateway to manage Model Context Protocol (MCP) servers and the tools they expose. By consolidating MCP server registration, tool discovery, and access control, MCPJungle enables AI agents and clients to discover, group, and securely invoke tools from a single, unified gateway. The project provides a CLI, Docker-based deployment options, and enterprise-ready features such as tool grouping, access control, and observability to streamline MCP-based workflows across organizations.

Playwright MCP server. A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables large language models (LLMs) to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models. The server is designed to be fast, lightweight, and deterministic, offering LLM-friendly tooling and a rich set of browser automation capabilities via MCP tools. It supports standalone operation, containerized deployments, and integration with a variety of MCP clients (Claude Desktop, VS Code, Copilot, Cursor, Goose, Windsurf, and others).

Playwright MCP server. A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables large language models (LLMs) to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models. The server is designed to be fast, lightweight, and deterministic, offering LLM-friendly tooling and a rich set of browser automation capabilities via MCP tools. It supports standalone operation, containerized deployments, and integration with a variety of MCP clients (Claude Desktop, VS Code, Copilot, Cursor, Goose, Windsurf, and others).

Sequential Thinking MCP Server provides a dedicated MCP tool that guides problem-solving through a structured, step-by-step thinking process. It supports dynamic adjustment of the number of thoughts and allows revision and branching within a controlled workflow, making it ideal for complex analysis and solution hypothesis development. This server is designed to register a single tool, sequential_thinking, and is integrated with common MCP deployment methods (NPX, Docker) as well as editor integrations like Claude Desktop and VS Code for quick setup. The documentation provides exact configuration snippets, usage patterns, and building instructions to help you deploy and use the MCP server effectively, including Codex CLI, NPX, and Docker installation examples.

Sequential Thinking MCP Server provides a dedicated MCP tool that guides problem-solving through a structured, step-by-step thinking process. It supports dynamic adjustment of the number of thoughts and allows revision and branching within a controlled workflow, making it ideal for complex analysis and solution hypothesis development. This server is designed to register a single tool, sequential_thinking, and is integrated with common MCP deployment methods (NPX, Docker) as well as editor integrations like Claude Desktop and VS Code for quick setup. The documentation provides exact configuration snippets, usage patterns, and building instructions to help you deploy and use the MCP server effectively, including Codex CLI, NPX, and Docker installation examples.

An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.

An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.

An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.

An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.

Hugging Face Official MCP Server connects your large language models (LLMs) to the Hugging Face Hub and thousands of Gradio AI Applications, enabling seamless MCP (Model Context Protocol) integration across multiple transports. It supports STDIO, SSE (to be deprecated but still commonly deployed), StreamableHTTP, and StreamableHTTPJson, with the Web Application allowing dynamic tool management and status updates. This MCP server is designed to be run locally or in Docker, and it provides integrations with Claude Desktop, Claude Code, Gemini CLI (and its extension), VSCode, and Cursor, making it easy to configure and manage MCP-enabled tools and endpoints. Tools such as hf_doc_search and hf_doc_fetch can be enabled to enhance document discovery, and an optional Authenticate tool can be included to handle OAuth challenges when called.

Hugging Face Official MCP Server connects your large language models (LLMs) to the Hugging Face Hub and thousands of Gradio AI Applications, enabling seamless MCP (Model Context Protocol) integration across multiple transports. It supports STDIO, SSE (to be deprecated but still commonly deployed), StreamableHTTP, and StreamableHTTPJson, with the Web Application allowing dynamic tool management and status updates. This MCP server is designed to be run locally or in Docker, and it provides integrations with Claude Desktop, Claude Code, Gemini CLI (and its extension), VSCode, and Cursor, making it easy to configure and manage MCP-enabled tools and endpoints. Tools such as hf_doc_search and hf_doc_fetch can be enabled to enhance document discovery, and an optional Authenticate tool can be included to handle OAuth challenges when called.

Shadcn UI v4 MCP Server is an advanced MCP (Model Context Protocol) server designed to give AI assistants comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata. It enables multi-framework support (React, Svelte, Vue, and React Native) with fast, cache-friendly access to component source code, demos, and directory structures, empowering AI-driven development workflows. The project emphasizes production-readiness with Docker Compose, SSE transport for multi-client deployments, and smart caching to optimize GitHub API usage while providing rich metadata and usage patterns for rapid prototyping and learning across frameworks.