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.

Blowback is an MCP server designed to enhance frontend development workflows by integrating with development servers like Vite. It captures browser console logs and manages them through a checkpoint system, enabling developers to query logs for specific versions. The server also provides tools for browser automation, HMR event tracking, and state management, making it a valuable addition to modern frontend development environments.

Blowback is an MCP server designed to enhance frontend development environments by integrating with tools like Cursor. It captures browser console logs, manages checkpoints, and provides tools for browser automation, HMR event tracking, and log management. The server supports structured data flow between the development server, browser, and MCP client, making it a powerful tool for debugging and state management in frontend projects.

MCPXcode is an open-source implementation of the Model Context Protocol (MCP) for Xcode, enabling AI-assisted automation and contextual understanding within the Xcode environment. It wraps common command-line tools like `xcrun` and `xctrace`, leveraging macOS accessibility features to streamline developer workflows. The project aims to enhance productivity by providing structured context exchange and programmable interactions between Xcode and AI tools.

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.

The Enhanced PostgreSQL MCP Server is a Model Context Protocol server that extends the capabilities of the original PostgreSQL MCP server by Anthropic. It provides both read and write access to PostgreSQL databases, allowing LLMs to inspect database schemas, execute queries, modify data, and manage database schema objects. This enhanced version includes features like data modification, schema creation, and transaction handling, making it a powerful tool for integrating LLMs with PostgreSQL databases.

This project is an MCP (Model-Controller-Presenter) server implemented in TypeScript, designed to analyze GitHub Pull Requests. It provides a structured approach to handling PR data, enabling efficient analysis and integration with GitHub workflows. The server is built using Node.js and npm, with a clear project structure and scripts for building and running the server.

The Solana MCP Server allows users to interact with the Solana blockchain using their private key. It provides features such as retrieving the latest slot, wallet address, wallet balance, and transferring SOL. The server can be integrated with Cursor for enhanced functionality and is built using JavaScript and Shell.

This repository provides a step-by-step guide to building a Model Context Protocol (MCP) server using Google's Gemini 2.0 model. It includes integration with Brave Search for web and local searches, offering a flexible architecture for AI-powered applications. The project showcases interoperability, modularity, and standardization in AI tool integrations.

This MCP server bridges Claude or other LLM clients with AutoCAD LT, allowing users to create engineering drawings through conversational prompts. It generates and executes AutoLISP code, supports basic shapes, block insertion, layer management, and custom AutoLISP code execution. The server facilitates text-to-CAD functionality, making it easier to control AutoCAD LT using natural language.

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.

The bioRxiv MCP Server bridges AI assistants and bioRxiv's preprint repository using the Model Context Protocol (MCP). It allows AI models to search for biology preprints, access metadata, and download content programmatically. The server supports advanced search, efficient metadata retrieval, and local storage for faster access.

The bioRxiv MCP Server bridges AI assistants with bioRxiv's preprint repository using the Model Context Protocol (MCP). It allows AI models to search for biology preprints and access their metadata programmatically. Key features include paper search, efficient retrieval, metadata access, and local storage for faster access.

The Code Index MCP Server is designed to help large language models (LLMs) efficiently index, search, and analyze code within project directories. It supports multiple programming languages, provides detailed file summaries, and allows for code structure and complexity analysis. The server integrates seamlessly with tools like Claude Desktop, offering persistent storage of project settings and automated dependency management using UV.

The MCP Local Router is a tool designed to serve as an aggregation proxy for MCP (Model Context Protocol) servers. It connects to multiple upstream MCP servers and consolidates their functionalities into a unified interface, simplifying the interaction for downstream clients. Features include support for configuration files, multiple upstream servers, stdio transport, and environment variable injection.