Blender Open MCP is a project that connects Blender with local AI models via Ollama, enabling natural language control over 3D modeling tasks. It uses the Model Context Protocol (MCP) for structured communication, supports basic 3D operations, and includes a Blender add-on for seamless integration. Optional PolyHaven integration allows downloading assets directly within Blender via AI prompts.

ZIP MCP Server is a compression and decompression tool based on the Model Context Protocol (MCP), leveraging fastMCP and zip.js. It provides fully parameter-controlled ZIP operations, including compression, decompression, and querying compressed package metadata. This tool is designed to integrate with AI clients, offering features like multi-file packaging, compression level control, password protection, and encryption strength settings.

The Google Scholar MCP Server provides a bridge between AI assistants and Google Scholar through the Model Context Protocol (MCP). It allows AI models to search for academic papers, retrieve metadata, and access author information programmatically. Key features include paper search, efficient retrieval, author information, and research support, making it a valuable tool for academic research and analysis.

This MCP server, implemented in Swift, provides seamless integration with Unsplash's photo library, enabling advanced search, retrieval, and random photo selection. It includes features like keyword-based search, color and orientation filters, and detailed photo information. The server is designed to be easily integrated with tools like Claude and Cursor, offering a powerful solution for managing Unsplash photos in AI workflows.

The Excel File Processing MCP Server is a microservice designed to handle Excel file operations such as reading, writing, and analyzing data. It provides functionalities like reading specific worksheets, creating new Excel files, analyzing file structures, and managing cache and logs. Built on the Model Context Protocol (MCP), it supports integration with various platforms and tools, including Smithery for automated installation.

This MCP server offers tools and resources to fetch and analyze the Crypto Fear & Greed Index, including current and historical data, trend analysis, and prompt generation. It integrates with MCP-compatible clients like Claude Desktop, making it easy to access and interpret cryptocurrency market sentiment.

The GitHub Notifications MCP Server is designed to help Open Source Software (OSS) maintainers manage their GitHub notifications efficiently. It integrates with AI assistants like Claude to allow users to list, filter, mark as read, and manage notifications using natural language commands. This server provides tools for handling notification threads, subscriptions, and repository-specific notifications, making it easier to stay on top of GitHub activity.

This MCP server is built with Node.js and TypeScript, offering access to Autodesk Platform Services API. It features secure service accounts for fine-grained access control and integrates with tools like Claude Desktop and the Model Context Protocol Inspector. It enables users to manage and query Autodesk Construction Cloud (ACC) or BIM360 projects programmatically.

The GitHub Enterprise MCP Server provides an interface to access and manage GitHub Enterprise resources such as repositories, issues, pull requests, and workflows through the Model Context Protocol (MCP). It supports integration with tools like Cursor and Claude Desktop, offering features like repository management, workflow automation, and enhanced error handling. Designed primarily for GitHub Enterprise Server environments, it also works with GitHub.com and GitHub Enterprise Cloud.

The Feishu MCP Server is designed to facilitate seamless integration between AI models and Feishu services through the Model Context Protocol. It provides features such as document services, bot messaging, chat management, and multi-mode support including STDIO and HTTP modes. Built with TypeScript, it ensures type safety and modularity, making it easy to extend and integrate with other Feishu APIs.

The MCP System Monitor is a monitoring tool designed to expose system metrics such as CPU, memory, disk, network, host, and process information through the Model Context Protocol (MCP). It enables large language models (LLMs) to retrieve real-time system data via an MCP-compatible interface, facilitating seamless integration and system monitoring capabilities for AI applications.

The DaVinci Resolve MCP Server facilitates two-way communication between AI assistants such as Claude and DaVinci Resolve using the Model Context Protocol. It allows for project management, timeline manipulation, media management, Fusion integration, and more. This server leverages DaVinci Resolve's Python API to provide seamless control and interaction capabilities, making it a powerful tool for AI-driven video editing workflows.

The OpenAPI to Model Context Protocol (MCP) proxy server dynamically translates OpenAPI specifications into standardized MCP tools, resources, and prompts. This simplifies integration by eliminating the need for custom API wrappers. It supports OAuth2 authentication, JSON-RPC 2.0 compliance, and generates contextual prompts to assist LLMs in understanding API usage.

The MCP server provides tools for managing Kubernetes clusters, including pod, deployment, and service management. It also offers workload analysis capabilities to monitor and optimize cluster performance. Built with Go, it requires kubectl configuration for seamless integration with Kubernetes environments.