The YouTube Data MCP Server provides a standardized interface for AI language models to interact with YouTube content. It offers features like retrieving video details, managing transcripts, analyzing channels, and tracking trends. The server supports multi-language transcripts, video engagement analysis, and trending content discovery, making it a comprehensive tool for YouTube data integration.

This TypeScript-based MCP server integrates with Notion to manage and interact with pages across a workspace. It allows users to list, retrieve, and search Notion pages as Markdown notes, and provides tools for summarizing, refactoring, and enhancing content. The server supports remote deployment and integrates with tools like Claude Desktop for seamless usage.

This project provides a Flutter-based server implementation for the Model Context Protocol (MCP), enabling integration with various platforms and services. It supports multiple platforms including Android, iOS, Linux, macOS, and Windows, making it versatile for cross-platform development. The server is designed to facilitate communication and data handling within applications using the MCP framework.

The OpenDigger MCP Server is designed to facilitate interaction between Large Language Models (LLMs) and OpenDigger data. By integrating with MCP tools, this server allows users to retrieve and analyze online data from OpenDigger, providing valuable insights. It serves as a bridge for LLMs to access structured data efficiently.

The MCP Tree-sitter Server is designed to enable Claude, an AI assistant, to intelligently access and analyze codebases with appropriate context management. It supports multiple programming languages, including Python, JavaScript, TypeScript, Go, Rust, and more, using tree-sitter for AST-based understanding. Features include flexible code exploration, context management, symbol extraction, dependency analysis, and caching for optimized performance.

The TradingView PineScript MCP Server is a Model Context Protocol (MCP) server designed to work with TradingView PineScript. It provides tools for validating PineScript code for syntax errors and warnings, automatically fixing common syntax errors, and generating validated templates for various PineScript strategies and indicators. This server facilitates seamless integration with PineScript development workflows, ensuring code quality and efficiency.

MCPWizard is a command-line interface (CLI) tool designed to simplify the creation, management, and deployment of Model Context Protocol (MCP) servers. It supports project initialization, tool management, server building, and deployment, along with generating Claude Desktop configuration files. The tool currently supports TypeScript and Python templates, with plans to expand features like MCP resources, prompts, and transport support.

The SearXNG MCP Server allows AI assistants to conduct web searches through SearXNG, a privacy-focused metasearch engine. It supports zero-configuration setup by automatically selecting a random public instance from SearX.space, while also offering private instance support with basic authentication. The server provides customizable search parameters, privacy-focused results, and markdown-formatted outputs, making it ideal for integration with AI tools like Claude and Smolagents.

The AniList MCP Server is a Model Context Protocol (MCP) implementation designed to interact with the AniList API. It enables LLM clients to search for anime, manga, characters, and staff, as well as access user profiles, lists, and advanced filtering options. The server supports authenticated operations like favoriting media and updating user lists, making it a powerful tool for integrating AniList data into AI workflows.

This project provides a server implementation that facilitates integration between Unity and the Model Context Protocol (MCP). It enables Unity applications to communicate with MCP-compatible services, enhancing the capabilities of Unity projects with advanced AI and model interactions.

This repository is a collection of Medical MCP (Model Context Protocol) servers that facilitate secure interactions between AI models and medical resources such as PubMed, medRxiv, DICOM, and FHIR data. These servers extend AI capabilities by providing access to medical databases, API integrations, and contextual services, making them valuable tools for healthcare and research applications.

The Flux Schnell Image Generation MCP Server is a TypeScript implementation that leverages Cloudflare's Flux Schnell worker API to generate images from text prompts. It provides a seamless integration with the Flux Schnell API, allowing users to input text descriptions and receive generated image files. The server is configurable via environment variables and supports both project-specific and global configurations in Cursor, making it versatile for various development environments.

This project implements a Model Context Protocol (MCP) server that bridges natural language interactions with Azure DevOps REST API. It allows AI assistants to manage work items, pipelines, pull requests, and more. Built with the MCP Python SDK and Azure DevOps Python API, it simplifies DevOps tasks through conversational AI.