Auth0 MCP Server is an open - source project that integrates Auth0 management functions into LLMs and AI agents through the Model Context Protocol, allowing users to perform Auth0 management operations through natural language.

The Model Context Protocol (MCP) is an open - source protocol that provides a series of reference implementations and community - developed servers. It aims to provide large language models (LLMs) with secure and controllable access to tools and data sources. These servers demonstrate the diversity and scalability of MCP, covering various functions from file system operations to database integration, from web search to AI image generation.

Trellis MCP is an interface service that connects AI assistants with Trellis 3D generation models, supporting rapid generation of 3D assets through natural language and importing them into Blender. This project is based on an open - source model and requires self - deployment of the API backend. It is fast and free, but there are stability risks.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

An open - source project that integrates Blender with local AI models to control 3D modeling through natural language.

The Model Context Protocol (MCP) is an open-source protocol that provides a series of reference implementations and community-built servers, enabling large language models (LLMs) to access tools and data sources securely and controllably. The project includes implementations of various functional servers, covering areas such as file systems, databases, search, and API integration, and supports development with TypeScript and Python SDKs.

The Model Context Protocol (MCP) is an open-source protocol designed to enable Large Language Models (LLMs) to seamlessly interact with external tools, services, and data sources through a standardized interface, simplifying the integration process and expanding AI capabilities.

The Model Context Protocol (MCP) is an open-source protocol that provides a series of reference implementations and community-built servers, enabling large language models (LLMs) to access tools and data sources securely and controllably. This project includes various types of server implementations, covering multiple domains such as file systems, databases, Git, and Slack, and supports TypeScript and Python SDKs.

MCP - OpenLLM is a LangChain wrapper tool for seamless integration of different MCP servers and open - source large language models (LLMs), supporting the use of LangChain community models.

Model Context Protocol (MCP) is an open - source protocol that provides a series of reference implementations and community - developed servers, enabling large language models (LLMs) to securely and controllably access tools and data sources. The project includes server implementations with various functions, such as file system operations, database access, Git management, Slack integration, etc., and supports TypeScript and Python SDKs.

The SCANOSS MCP Server is a service integrating SCANOSS open - source scanning technology. It provides code snippet scanning, open - source license compliance checking, and component analysis functions for language models through the Model Context Protocol.

This is a list of multiple open - source AI projects, covering multiple fields such as automated agents, large - language models, image generation, and AI development frameworks. These projects aim to help developers make money using AI technology, including building intelligent assistants, automating workflows, and content generation applications.

Model Context Protocol (MCP) is an open - source protocol that provides a series of reference implementations and community - developed servers for providing large language models (LLMs) with secure and controlled access to tools and data sources. The project includes various functional servers, such as file system operations, Git integration, database access, etc., and supports rapid development of new services through TypeScript and Python SDKs.

Model Context Protocol (MCP) is an open-source protocol that provides a series of reference implementations and community-developed servers, enabling large language models (LLMs) to securely and controllably access tools and data sources. The project includes various types of server implementations covering multiple domains such as file systems, databases, Git, and Slack, demonstrating the diversity and scalability of MCP.

This project provides a Python - based MCP server for integrating OSV (open - source vulnerability) data with AI assistants or large - language models, and exposes tools and resources through the MCP protocol to enhance the workflows and capabilities of LLMs.

The Model Context Protocol (MCP) is an open - source protocol that provides a standardized method for large language models to connect to external data sources and tools. It supports functions such as resources, prompts, tools, and sampling. This article focuses on how to develop and use MCP tool services.