Top MCPs by Category

A Model Context Protocol (MCP) server that enables AI assistants to interact with Anki, the spaced repetition flashcard application. The Anki MCP Server allows AI models to access Anki's card data, enabling features like automated flashcard creation, review, and management.

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. The 1mcp-app/agent is an open-source project that provides a single entry point for multiple MCP servers, making it easier to manage and interact with various AI models and tools.

Roundtable AI MCP Server is a zero-configuration local MCP server that unifies multiple AI coding assistants (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and a standardized interface. It coordinates specialized sub-agents from within your IDE to solve engineering problems in parallel, sharing context and synthesizing responses into a single, high-quality output. This documentation details installation, available MCP tools, integration with popular IDEs, and a broad ecosystem of specialized tools and CLIs that can be invoked as part of a roundtable-powered workflow, enabling developers to delegate tasks to the right AI for each facet of a problem without leaving their development environment.

mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.

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.

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.

Anyquery MCP is the Model Context Protocol endpoint for the Anyquery SQL engine, enabling large language models (LLMs) like ChatGPT and Claude to connect to and query data through MCP. This MCP server acts as a bridge between LLMs and Anyquery’s data integrations, allowing LLMs to contextually access files, databases, and apps via the MCP interface. It complements Anyquery’s SQL querying capabilities by providing a standardized, secure channel for LLMs to request data access, execute SQL-like interactions, and receive structured results. The MCP integration is designed to be used in conjunction with Anyquery’s SQL runtime, including its MySQL-compatible server mode for client tooling, and is part of the broader MCP-enabled ecosystem described in the project docs.

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.

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.

mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.

NCP is a unified MCP platform that consolidates 50+ tools, skills, and Photons into a single, intelligent interface. It enables code-mode execution, on-demand loading, scheduling, and semantic tool discovery, dramatically reducing token usage and latency while enabling AI assistants to work with external MCPs, skills, and Photons. This documentation covers how NCP works, the available MCPs and tools, installation and integration steps for popular clients (Claude Desktop, VS Code, and more), and practical examples that demonstrate how to find, run, and compose tools across MCPs. Whether you’re building with internal MCPs or exploring external tools, NCP provides a scalable, vendor-agnostic foundation for AI-powered automation and tool orchestration.

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.

The MCP server for Appwrite docs enables LLMs and code-generation tools to interact with comprehensive Appwrite documentation. It empowers AI assistants to access up-to-date API references, SDK guides, and implementation examples, facilitating intelligent code generation, troubleshooting, and best-practice guidance directly from the official docs. This MCP brings real-time context, semantic search, and seamless integration with popular editors and IDEs to accelerate development workflows around Appwrite's APIs and SDKs.

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.

mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.

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.

GitHub's official MCP Server. This repository hosts the MCP server implementation that enables Model Context Protocol (MCP) tooling for GitHub data and workflows. It exposes a wide registry of MCP tools spanning code management, repository operations, issues, pull requests, workflows, gists, and more. The documentation and commit history reveal a broad set of tools (GetMe, GetTeams, ListIssues, CreateOrUpdateFile, GetRepositoryTree, and many others) that are designed to be wired into dynamic toolsets and accessed via a consistent ServerTool pattern. This MCP server is built with extensibility in mind, supporting features like tool dependencies, dynamic toolsets, and feature flags to adapt to varied prompts and use cases. The project emphasizes a registry-driven approach where tools, resources, and prompts are defined and validated, enabling robust integration with client apps and AI models.

The Figma MCP server enables design context delivery from Figma files to AI agents and code editors, empowering teams to generate code directly from design selections. It supports both a remote hosted server and a locally hosted desktop server, allowing seamless integration with popular editors through Code Connect and a suite of tools that extract design context, metadata, variables, and more. This guide covers enabling the MCP server, configuring clients (VS Code, Cursor, Claude Code, and others), and using a curated set of MCP tools to fetch structured design data for faster, more accurate code generation. It also explains best practices, prompts, and integration workflows that help teams align generated output with their design systems. The documentation includes concrete JSON examples for configuring servers in editors like VS Code and Cursor, as well as command examples for Claude Code integration and plugin installation.

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.

A Model Context Protocol (MCP) server that enables AI assistants to interact with Anki, the spaced repetition flashcard application. The Anki MCP Server allows AI models to access Anki's card data, enabling features like automated flashcard creation, review, and management.

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. The 1mcp-app/agent is an open-source project that provides a single entry point for multiple MCP servers, making it easier to manage and interact with various AI models and tools.

mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.

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.

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. The 1mcp-app/agent is an open-source project that provides a single entry point for multiple MCP servers, making it easier to manage and interact with various AI models and tools.

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.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

TrendRadar MCP is an AI-driven Model Context Protocol (MCP) based analysis server that exposes a suite of specialized tools for cross-platform news analysis, trend tracking, and intelligent push notifications. It integrates with TrendRadar’s multi-platform data aggregation (RSS and trending topics) and provides advanced AI-powered insights, sentiment analysis, and cross-platform correlation. The MCP server enables developers to query, analyze, and compare news across platforms using a consistent toolset, with ongoing updates that expand capabilities such as RSS querying, date parsing, and multi-date trend analysis. This documentation references the MCP module updates, tool additions, and architecture changes that enhance extensibility, cross-platform data handling, and AI-assisted reporting.

Roundtable AI MCP Server is a zero-configuration local MCP server that unifies multiple AI coding assistants (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and a standardized interface. It coordinates specialized sub-agents from within your IDE to solve engineering problems in parallel, sharing context and synthesizing responses into a single, high-quality output. This documentation details installation, available MCP tools, integration with popular IDEs, and a broad ecosystem of specialized tools and CLIs that can be invoked as part of a roundtable-powered workflow, enabling developers to delegate tasks to the right AI for each facet of a problem without leaving their development environment.

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.

PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services. The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.

pageguard-mcp is an MCP (Model Context Protocol) server that exposes PageGuard privacy compliance scanning as a set of tools for AI coding assistants. It enables seamless integration with Claude Code, Cursor, Windsurf, ChatGPT, and any MCP-compatible environment. The server supports local scans, live URL scans, and AI-generated privacy-related documents, helping developers identify tracking technologies, cookies, and third-party data processing, while also producing tailored privacy policies and compliance materials. With its straightforward MCP configuration and free local scanning capability, pageguard-mcp is designed to empower teams to maintain privacy compliance across their projects and websites.

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.

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.

OpenClaw MCP Server is a secure Model Context Protocol (MCP) bridge that connects Claude.ai with a self-hosted OpenClaw assistant, enabling OAuth2 authentication and safe, controlled communication between the Claude AI ecosystem and your local or hosted OpenClaw deployment. This MCP server acts as an orchestration layer that exposes MCP tools to Claude.ai, manages authentication, and enforces security boundaries like CORS and transport options. It is designed to be deployed via Docker or run locally, with detailed installation, configuration, and security guidance provided in the documentation. By serving as a bridge, it enables Claude.ai to delegate tasks to your OpenClaw bot while preserving control over data flow and access controls, in line with MCP specifications and best security practices.

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.

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.

MarkItDown-MCP is a lightweight MCP (Model Context Protocol) server provided as the markitdown-mcp package. It exposes a STDIO, Streamable HTTP, and SSE MCP server designed for calling MarkItDown to convert content to Markdown. The package focuses on simplicity and accessibility, enabling you to run the MCP server locally via a simple CLI, or in Docker for containerized workflows, with integration options for Claude Desktop. The core capability is exposed through a single tool, convert_to_markdown(uri), which accepts a URI in http:, https:, file:, or data: schemes to fetch content and convert it to Markdown. This MCP server is easy to install with pip and can be used in various transport modes, including STDIO and HTTP/SSE, making it a flexible choice for automations and integrations.

The ChainAware Behavioural Prediction MCP is an MCP-based server that provides AI-powered tools to analyze wallet behaviour prediction, fraud detection, and rug pull prediction. Designed for Web3 security and DeFi analytics, it enables developers and platforms to integrate risk assessment, predictive wallet behavior insights, and rug-pull detection through MCP-compatible clients. The server exposes three specialized tools and uses Server-Sent Events (SSE) for real-time responses, helping safeguard DeFi users, monitor liquidity risks, and score wallet or contract trustworthiness. Access to production endpoints is API-key gated, reflecting a private backend architecture that supports secure, scalable risk analytics across wallets, contracts, and pools.

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.

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.

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.

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).

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.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

MarkItDown-MCP is a lightweight MCP (Model Context Protocol) server provided as the markitdown-mcp package. It exposes a STDIO, Streamable HTTP, and SSE MCP server designed for calling MarkItDown to convert content to Markdown. The package focuses on simplicity and accessibility, enabling you to run the MCP server locally via a simple CLI, or in Docker for containerized workflows, with integration options for Claude Desktop. The core capability is exposed through a single tool, convert_to_markdown(uri), which accepts a URI in http:, https:, file:, or data: schemes to fetch content and convert it to Markdown. This MCP server is easy to install with pip and can be used in various transport modes, including STDIO and HTTP/SSE, making it a flexible choice for automations and integrations.

TrendRadar MCP is an AI-driven Model Context Protocol (MCP) based analysis server that exposes a suite of specialized tools for cross-platform news analysis, trend tracking, and intelligent push notifications. It integrates with TrendRadar’s multi-platform data aggregation (RSS and trending topics) and provides advanced AI-powered insights, sentiment analysis, and cross-platform correlation. The MCP server enables developers to query, analyze, and compare news across platforms using a consistent toolset, with ongoing updates that expand capabilities such as RSS querying, date parsing, and multi-date trend analysis. This documentation references the MCP module updates, tool additions, and architecture changes that enhance extensibility, cross-platform data handling, and AI-assisted reporting.

SafeAgent is a security-first MCP server that gives AI agents the ability to check if a cryptocurrency token is safe before trading. **What it does:** - Honeypot detection — simulates real DEX buy+sell to verify you can actually sell - Scam pattern analysis — 17 patterns including hidden mint, fee manipulation, blacklists - Owner analysis — checks if ownership is renounced or if owner has dangerous functions - Proxy detection — flags upgradeable contracts - Source code audit — deep analysis of verified contracts - Multi-chain — supports Base, Ethereum, Arbitrum, Optimism, Polygon, BSC **Why agents need this:** Every AI agent that trades tokens needs to verify safety BEFORE buying. One API call prevents buying a honeypot or rug pull. SafeAgent provides a safety score (0-100), verdict (LIKELY SAFE / MODERATE RISK / LIKELY SCAM), and detailed flags. **Also includes:** - DeFi yield scoring (17K+ pools, quality grades A-F) - Market overview (TVL, top gainers/losers, risk alerts)

Prop Firm Deal Finder (PFDF) is a free MCP server that gives AI assistants real-time access to live discount codes across 20+ proprietary trading firms. It provides 6 tools: get_deals (all current discounts), search_firms (search by name/asset class), compare_firms (side-by-side comparison of challenges), find_cheapest (cheapest challenge by account size with PFDF code applied), get_firm_details (15+ data points per firm), and get_discount_code (universal PFDF code). Covers firms like Topstep, Apex Trader Funding, MyFundedFutures, FTMO, The Funded Trader, and 14 more. No API key required. Install via npx propfirmdealfinder-mcp-server or connect remotely at https://web-production-6607c.up.railway.app/mcp

MCP server for financial data and market intelligence. Access stock screeners, currency exchange rates, cryptocurrency prices, and financial news. Aggregate data from multiple financial sources for comprehensive market analysis. Built on Apify cloud with Streamable HTTP transport. Connect via: https://nexgendata--finance-mcp-server.apify.actor/mcp

AudioAlpha turns 100+ daily finance and crypto podcasts into structured intelligence — α-sentiment scores, narrative signals, asset mentions, transcripts, and market snapshots with 40+ custom metrics. Built for AI-driven research and trading workflows.

Prop Firm Deal Finder (PFDF) is a free MCP server that gives AI assistants real-time access to live discount codes across 20+ proprietary trading firms. It provides 6 tools: get_deals (all current discounts), search_firms (search by name/asset class), compare_firms (side-by-side comparison of challenges), find_cheapest (cheapest challenge by account size with PFDF code applied), get_firm_details (15+ data points per firm), and get_discount_code (universal PFDF code). Covers firms like Topstep, Apex Trader Funding, MyFundedFutures, FTMO, The Funded Trader, and 14 more. No API key required. Install via npx propfirmdealfinder-mcp-server or connect remotely at https://web-production-6607c.up.railway.app/mcp

MCP server for financial data and market intelligence. Access stock screeners, currency exchange rates, cryptocurrency prices, and financial news. Aggregate data from multiple financial sources for comprehensive market analysis. Built on Apify cloud with Streamable HTTP transport. Connect via: https://nexgendata--finance-mcp-server.apify.actor/mcp

Get a crypto proof of funds letter — from inside your AI assistant. RealOpen is a crypto real estate platform that lets buyers purchase homes with Bitcoin, Ethereum, stablecoins, and other crypto, closing as cash buyers. Connect this MCP server to verify crypto wallets, generate proof of funds letters, and learn everything about buying real estate with crypto — fees, supported assets, timelines, and the full transaction flow.

AudioAlpha turns 100+ daily finance and crypto podcasts into structured intelligence — α-sentiment scores, narrative signals, asset mentions, transcripts, and market snapshots with 40+ custom metrics. Built for AI-driven research and trading workflows.

Roundtable AI MCP Server is a zero-configuration local MCP server that unifies multiple AI coding assistants (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and a standardized interface. It coordinates specialized sub-agents from within your IDE to solve engineering problems in parallel, sharing context and synthesizing responses into a single, high-quality output. This documentation details installation, available MCP tools, integration with popular IDEs, and a broad ecosystem of specialized tools and CLIs that can be invoked as part of a roundtable-powered workflow, enabling developers to delegate tasks to the right AI for each facet of a problem without leaving their development environment.

Context7 MCP Server delivers up-to-date, code-first documentation and examples for LLMs and AI code editors by pulling content directly from the source. It supports multiple MCP clients and exposes tools that help you resolve library IDs and retrieve library documentation, ensuring prompts use current APIs and usage patterns. The repository provides installation and integration guides for Cursor, Claude Code, Opencode, and other clients, along with practical configuration samples and OAuth options for remote HTTP connections. This MCP server is designed to keep prompts in sync with the latest library docs, reducing hallucinations and outdated code snippets.

pageguard-mcp is an MCP (Model Context Protocol) server that exposes PageGuard privacy compliance scanning as a set of tools for AI coding assistants. It enables seamless integration with Claude Code, Cursor, Windsurf, ChatGPT, and any MCP-compatible environment. The server supports local scans, live URL scans, and AI-generated privacy-related documents, helping developers identify tracking technologies, cookies, and third-party data processing, while also producing tailored privacy policies and compliance materials. With its straightforward MCP configuration and free local scanning capability, pageguard-mcp is designed to empower teams to maintain privacy compliance across their projects and websites.

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.

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.

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.

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.

OpenMCP is a dual-purpose framework: a standard for converting web APIs into MCP servers and an open-source registry of servers that follow that standard. Each OpenMCP server exposes a token-efficient MCP interface that enables MCP clients to make requests to a target web API on behalf of users. Together, the servers in the registry enable client LLMs to fetch data and perform actions across a broad set of domains, providing a scalable, interoperable way to integrate external services with MCP clients. The documentation guides you through creating a server, adding it to MCP clients, and converting various web API formats into OpenMCP-compatible servers, covering REST, gRPC, JSON-RPC, GraphQL, SOAP, and PostgREST variants.

GitHub's official MCP Server. This repository hosts the MCP server implementation that enables Model Context Protocol (MCP) tooling for GitHub data and workflows. It exposes a wide registry of MCP tools spanning code management, repository operations, issues, pull requests, workflows, gists, and more. The documentation and commit history reveal a broad set of tools (GetMe, GetTeams, ListIssues, CreateOrUpdateFile, GetRepositoryTree, and many others) that are designed to be wired into dynamic toolsets and accessed via a consistent ServerTool pattern. This MCP server is built with extensibility in mind, supporting features like tool dependencies, dynamic toolsets, and feature flags to adapt to varied prompts and use cases. The project emphasizes a registry-driven approach where tools, resources, and prompts are defined and validated, enabling robust integration with client apps and AI models.

NCP is a unified MCP platform that consolidates 50+ tools, skills, and Photons into a single, intelligent interface. It enables code-mode execution, on-demand loading, scheduling, and semantic tool discovery, dramatically reducing token usage and latency while enabling AI assistants to work with external MCPs, skills, and Photons. This documentation covers how NCP works, the available MCPs and tools, installation and integration steps for popular clients (Claude Desktop, VS Code, and more), and practical examples that demonstrate how to find, run, and compose tools across MCPs. Whether you’re building with internal MCPs or exploring external tools, NCP provides a scalable, vendor-agnostic foundation for AI-powered automation and tool orchestration.

PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services. The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.

MarkItDown-MCP is a lightweight MCP (Model Context Protocol) server provided as the markitdown-mcp package. It exposes a STDIO, Streamable HTTP, and SSE MCP server designed for calling MarkItDown to convert content to Markdown. The package focuses on simplicity and accessibility, enabling you to run the MCP server locally via a simple CLI, or in Docker for containerized workflows, with integration options for Claude Desktop. The core capability is exposed through a single tool, convert_to_markdown(uri), which accepts a URI in http:, https:, file:, or data: schemes to fetch content and convert it to Markdown. This MCP server is easy to install with pip and can be used in various transport modes, including STDIO and HTTP/SSE, making it a flexible choice for automations and integrations.

The MCP server for Appwrite docs enables LLMs and code-generation tools to interact with comprehensive Appwrite documentation. It empowers AI assistants to access up-to-date API references, SDK guides, and implementation examples, facilitating intelligent code generation, troubleshooting, and best-practice guidance directly from the official docs. This MCP brings real-time context, semantic search, and seamless integration with popular editors and IDEs to accelerate development workflows around Appwrite's APIs and SDKs.

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.

Context7 MCP Server delivers up-to-date, code-first documentation and examples for LLMs and AI code editors by pulling content directly from the source. It supports multiple MCP clients and exposes tools that help you resolve library IDs and retrieve library documentation, ensuring prompts use current APIs and usage patterns. The repository provides installation and integration guides for Cursor, Claude Code, Opencode, and other clients, along with practical configuration samples and OAuth options for remote HTTP connections. This MCP server is designed to keep prompts in sync with the latest library docs, reducing hallucinations and outdated code snippets.

The MCP server for Appwrite docs enables LLMs and code-generation tools to interact with comprehensive Appwrite documentation. It empowers AI assistants to access up-to-date API references, SDK guides, and implementation examples, facilitating intelligent code generation, troubleshooting, and best-practice guidance directly from the official docs. This MCP brings real-time context, semantic search, and seamless integration with popular editors and IDEs to accelerate development workflows around Appwrite's APIs and SDKs.

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).

Context7 MCP Server delivers up-to-date, code-first documentation and examples for LLMs and AI code editors by pulling content directly from the source. It supports multiple MCP clients and exposes tools that help you resolve library IDs and retrieve library documentation, ensuring prompts use current APIs and usage patterns. The repository provides installation and integration guides for Cursor, Claude Code, Opencode, and other clients, along with practical configuration samples and OAuth options for remote HTTP connections. This MCP server is designed to keep prompts in sync with the latest library docs, reducing hallucinations and outdated code snippets.

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.

Skyvern is the complete browser MCP for AI agents. 75+ tools for clicking, filling forms, extracting structured data, logging in with 2FA, uploading files, drag-and-drop, running JavaScript, inspecting network traffic, multi-tab browsing, and building reusable cached workflows. First workflow run uses AI; subsequent runs replay a cached script with zero LLM calls.

MCP server for real-time news aggregation and search. Access headlines, full articles, and trending topics from major news sources worldwide. Supports keyword search, category filtering, and multi-source aggregation. Built on Apify cloud infrastructure with Streamable HTTP transport for seamless AI agent integration. Connect via: https://nexgendata--news-mcp-server.apify.actor/mcp