The Adfin MCP Server facilitates seamless integration between Claude Desktop and Adfin APIs, allowing users to perform financial operations such as credit control checks, invoice creation, and bulk invoice uploads. It leverages Python and the uv package manager to ensure smooth operation and automatic updates of Adfin API tools within the Claude interface.

The PineScript Trading Strategy MCP Server provides a comprehensive toolset for developing and managing trading strategies using TradingView PineScript. It includes features such as strategy creation, backtesting, performance analysis, and optimization. The project offers multiple UI options, including a Next.js web interface, an Express server, and an Electron desktop application, ensuring flexibility in deployment and usage.

This project offers a natural language interface to MLflow, enabling users to interact with their MLflow tracking server using plain English. It consists of two main components: the MLflow MCP Server, which connects to the MLflow tracking server, and the MLflow MCP Client, which allows users to make natural language queries. Features include natural language queries, model registry exploration, experiment tracking, and system information retrieval. It leverages OpenAI models for natural language understanding and is designed to simplify MLflow management.

This MCP server implementation facilitates seamless integration between Google Calendar and Claude Desktop, allowing users to manage calendar events through natural language interactions. It supports event retrieval, creation, updates, and deletions, leveraging Google Calendar API and OAuth2 authentication for secure access. Built with TypeScript, it ensures robust and type-safe development.

This project provides a Model Context Protocol (MCP) server implementation specifically designed for Harvest, a time tracking and project management tool. It acts as a wrapper for the Harvest API, offering a standardized way for MCP clients to interact with Harvest. The server facilitates seamless integration and simplifies communication between Harvest and other systems through the MCP framework.

This project is an AI-powered file download manager built on the Model Context Protocol (MCP). It allows AI models to manage file downloads efficiently through a standardized interface, supporting features like multi-threaded downloads, task management, and real-time progress monitoring. The tool is designed to simplify download operations for users by enabling AI assistants to handle complex tasks such as starting, pausing, resuming, and canceling downloads without manual intervention.

The 1Panel MCP Server is designed to automate the deployment of websites to 1Panel servers. It creates websites if they don't already exist and uploads static website files seamlessly. Fully compatible with the Model Context Protocol (MCP) standard, this server integrates with 1Panel's API to streamline deployment processes. It is currently in the experimental phase, offering a practical solution for developers looking to automate their website deployment workflows.

This MCP server integrates with Figma to provide AI coding agents, such as Cursor, with access to design data. It simplifies and translates Figma API responses to deliver only the most relevant layout and styling information, improving the accuracy and relevance of AI-generated code. The server supports tools like Cursor, Windsurf, and Cline, enabling seamless design-to-code workflows.

The MLflow MCP Server connects to your MLflow tracking server and exposes MLflow functionality through the Model Context Protocol (MCP). It allows users to query their MLflow tracking server using plain English, making it easier to manage and explore machine learning experiments and models. The server includes features like natural language queries, model registry exploration, experiment tracking, and system information retrieval.

The PowerPoint Presentation MCP Server is a project designed to automate the creation of PowerPoint presentations. It includes tools for adding slides, tables, charts, and images, leveraging the Stable Diffusion API for image generation. Forked from supercurses/powerpoint, it extends the original project with additional features and integrations, making it a versatile tool for generating dynamic presentations programmatically.

This repository provides a set of Model Context Protocol (MCP) server implementations designed to enable Large Language Models (LLMs) to interact with DevOps systems. These servers offer a standardized way to automate and control infrastructure, deployment pipelines, monitoring, and other DevOps operations. Each implementation includes comprehensive tools that map to the respective DevOps platform's API, allowing LLMs to perform complex operations through simple function calls.

The OBS Studio Control via MCP Server provides tools to manage and control OBS Studio via the OBS WebSocket protocol. It supports general operations, scene management, source control, scene item manipulation, streaming, recording, and transitions. This server is designed to integrate with Claude desktop, allowing users to control OBS Studio programmatically.

This MCP server integrates with Groq's API to call LLMs, exposing raw chain-of-thought tokens from Qwen's qwq model. It enhances AI performance by enabling external 'think' tools, particularly useful in complex tool-use scenarios like those tested on SWE Bench. The server is designed to be easily configured and used with AI agents to improve reasoning and decision-making processes.

The MCP Kubernetes Server enables seamless management of Kubernetes clusters using the Model Context Protocol (MCP). It provides a natural language interface for performing common Kubernetes operations, such as creating deployments, scaling resources, and updating configurations. This server integrates with Large Language Models (LLMs) to simplify Kubernetes management, reduce command complexity, and ensure type-safe interactions.

This lightweight utility ensures API keys for Cursor MCP servers are securely loaded from a file in your home directory, avoiding accidental exposure in repositories. It maintains seamless integration with AI coding assistants by injecting keys as environment variables only when needed, enhancing security without disrupting workflow.

This project provides a complete PHP-based implementation of the Model Control Protocol (MCP) server framework. It supports annotation-based service definitions, including Tool, Prompt, and Resource processors. The framework also includes a comprehensive logging system and Docker support, making it easy to deploy and manage MCP services.

The GitLab MCP Server is a Model Context Protocol (MCP) implementation designed to integrate with GitLab, providing tools to manage repositories, issues, merge requests, wikis, and more. It supports both stdio and SSE transports, offers comprehensive GitLab API integration, and includes features like repository search, file operations, branch management, and wiki handling. This server is ideal for developers looking to streamline GitLab interactions within their workflows.