The Vidu Video Generation MCP Server provides a standardized interface for interacting with the Vidu video generation API. It enables users to convert static images into videos, monitor generation tasks, and upload images for processing. The server handles authentication, file uploads, and asynchronous task management, simplifying the integration with Vidu's AI models.

This server offers API tools that can be used by Microsoft Copilot or other AI assistants supporting the MCP protocol. It includes functionalities for querying Chinese criminal law articles, retrieving weather alerts and forecasts, querying Azure service prices, and counting Chinese characters in text. The server is built using FastMCP framework, Uvicorn ASGI server, FastAPI/Starlette web framework, and supports Server-Sent Events for communication.

The ClickUp Integration MCP Server provides a standardized interface for AI assistants to interact with the ClickUp API. It enables AI systems to access and manipulate ClickUp data such as workspaces, spaces, folders, lists, tasks, docs, comments, and checklists. This server simplifies the integration process, allowing developers to focus on building AI-driven workflows within ClickUp.

The Aiven MCP Server provides access to Aiven's PostgreSQL, Kafka, ClickHouse, Valkey, and OpenSearch services, integrating them into the Aiven ecosystem. It enables Large Language Models (LLMs) to build comprehensive solutions for various use cases. The server supports tools like listing projects, services, and fetching service details, making it a powerful backend for AI-driven applications.

The Jira MCP Server is a Model Context Protocol (MCP) implementation that allows users to interact with Jira's REST API using natural language commands. It integrates with Claude Desktop and other MCP clients, enabling features like fetching project details, searching issues, creating new issues, adding comments, and transitioning issue statuses. The server supports multiple authentication methods and can be run locally or via Docker.

This MCP server is designed to provide real-time cryptocurrency price data, market trends, and detailed information. It integrates with APIs like CoinGecko, Bitget, and Coinglass to fetch data such as coin prices, K-line data, and market summaries. The server supports various installation methods, including pip, Smithery, and manual setup, making it versatile for different use cases.

The TMDB MCP Server is a Model Context Protocol (MCP) implementation that enables AI assistants to search and retrieve movie information from The Movie Database (TMDB). It supports searching movies by title, year, and other criteria, and provides detailed movie information. The server is designed for easy integration with MCP-compatible AI assistants and requires a TMDB API key for configuration.

The TweetBinder MCP Server is built on the Model Context Protocol (MCP) and allows Claude and other MCP-compatible AI models to access TweetBinder by Audiense analytics data. It provides features like analyzing hashtags, users, and conversations on Twitter, retrieving engagement metrics, sentiment analysis, and generating custom reports. This server bridges the gap between AI models and social media analytics, enabling deeper insights into Twitter data.

The Image Analysis MCP Server is designed to accept image URLs and analyze their content using the GPT-4-turbo model. It provides high-precision image recognition and detailed descriptions, along with image URL validity checks. This server is particularly useful for integrating image analysis capabilities into tools like Claude and VSCode extensions, enabling advanced image understanding in various applications.

The eClass MCP Server is designed to facilitate interactions between AI agents and the Open eClass platform. It supports UoA's CAS SSO authentication system, allowing AI agents to authenticate, retrieve course information, and manage sessions. The server is modular, with features like course management, session handling, and status checking, making it a powerful tool for integrating AI capabilities with eClass.

This MCP server enables large language models to interact with Azure Log Analytics by converting natural language queries into Kusto Query Language (KQL). It leverages Claude AI for query translation and provides both CLI and server modes for flexibility. The server formats query results for easy consumption by LLMs, making it a powerful tool for integrating natural language processing with Azure's logging capabilities.

This Python MCP Server Template provides a quick and efficient way to create servers that can register and expose tools and prompts for AI models. It includes features like tool and prompt creation, Docker support, and cloud deployment options, making it ideal for developers working with AI models. The template is designed to streamline the development process and ensure compatibility with various deployment environments.

The Agentis MCP Framework is a flexible Python framework designed for creating AI agents that connect to MCP servers for tool access and resource retrieval. It supports multi-agent workflows, offers a simple and intuitive API, and provides flexible configuration options. The framework is compatible with any MCP server and model provider, enabling developers to build powerful AI agents with ease.

This project enhances the MCP server by enabling it to function as a worker, facilitating better message handling and processing. It uses `proxyMessage` to pass messages to an existing MCP server, improving integration and scalability in distributed systems.