MCP Registry

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.

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.

Neurolink includes an External MCP Server capability, enabling seamless integration with external Model Context Protocol (MCP) servers. This feature loads and manages external MCP servers from a dedicated configuration file (.mcp-config.json), enables real JSON-RPC based communication, and supports end-to-end tool execution within the NeuroLink platform. It is designed for multi-provider AI workflows, allowing providers to delegate tool execution to external servers while preserving type safety, robust error handling, and deterministic behavior. The documentation highlights how to configure external MCP servers, register and discover tools, and perform end-to-end tool execution through the CLI, ensuring a production-ready MCP ecosystem.

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.

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.

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.

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.

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.

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.

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.