This MCP server provides seamless integration with the Giphy API, enabling AI models to search, retrieve, and utilize GIFs. It supports multiple search methods, including query-based, random, and trending GIF retrieval, with features like content filtering, optimized response formats, and comprehensive metadata. The server is designed for efficient API use and is compatible with AI tools like Claude Desktop.

The Omg Flux MCP Server is designed to interact with OhMyGPT's Flux API, enabling users to generate images programmatically. It provides a streamlined setup process with Node.js, allowing developers to easily clone, install, and configure the server. The server is built using JavaScript and TypeScript, making it versatile and efficient for handling image generation tasks.

The Firecrawl MCP Server is a powerful implementation of the Model Context Protocol (MCP) that integrates with Firecrawl to provide advanced web scraping capabilities. It supports features like URL discovery, content extraction, batch processing, and deep research, making it ideal for LLM clients such as Cursor and Claude. The server includes tools for efficient batch processing, rate limiting, and credit usage monitoring, ensuring seamless integration and optimal performance.

The PubMed MCP Server bridges AI assistants and PubMed's biomedical literature repository using the Model Context Protocol (MCP). It allows AI models to search for scientific articles, access metadata, and perform deep analysis programmatically. Key features include paper search, efficient retrieval, metadata access, research support, full-text PDF access, and deep paper analysis.

This MCP server extends the knowledge of AI assistants beyond their training data by allowing them to query and retrieve information from custom document collections. It processes Markdown, text, and PDF files into a searchable database, supporting both free and paid embedding models. Built with Docker, it ensures AI assistants stay updated with the latest information from private or technical sources.

The MCP Server Manager provides a unified interface to connect and manage multiple MCP servers through a single proxy. It supports automatic tool discovery, server configuration via JSON, and consistent naming for proxied tools. This solution simplifies the integration and management of multiple MCP servers, enabling seamless tool registration and usage across different servers.

This MCP server facilitates secure, read-only queries on Snowflake databases, allowing Claude to interact with Snowflake data without modifying it. It supports Python 3.12+, uses service account authentication for secure connections, and includes tools for listing databases, views, and executing custom read-only SQL queries. The server integrates seamlessly with Claude Desktop via stdio-based communication.

This MCP server provides a web-based interface for executing shell commands on virtual machines (VMs) through the Model Context Protocol (MCP). It includes automatic tunneling, allowing remote access to the VM from anywhere. Features include a web terminal, WebSocket-based real-time communication, and support for executing commands and creating web tunnels.

This MCP server facilitates integration between Claude and WhatsApp using the Evolution API. It supports functionalities like managing WhatsApp instances, sending messages, handling contacts and groups, and configuring webhooks. The server is designed for automation and is deployable via Docker, Docker Compose, or locally using Bun.

This MCP server enables AI assistants such as Claude to analyze code quality by running pylint and pytest checks. It generates smart prompts for LLMs to interpret issues and suggest fixes, all within a specified project directory for security. The server integrates seamlessly with AI tools, transforming debugging workflows by allowing natural language interaction for code analysis and improvement.

This repository provides a variety of MCP (Model-Context-Protocol) server implementations, including services for Git operations such as repository initialization, branch management, and remote operations. It also includes a comprehensive tutorial for developing MCP services from scratch, covering basic concepts, service design principles, and environment setup. The project is structured to facilitate easy addition of new modules and contributions.

The LSP MCP Server is a Model Context Protocol (MCP) implementation that acts as a bridge between Language Server Protocol (LSP) servers and Large Language Models (LLMs). It enables LLMs to query LSP servers for hover information, code completions, diagnostics, and code actions, providing more accurate and context-aware code suggestions. The server supports real-time updates, comprehensive logging, and runtime-configurable log levels, making it a powerful tool for integrating LSP capabilities into AI-driven development workflows.

This MCP server allows AI assistants to securely interact with MySQL databases, listing tables, reading data, and executing SQL queries through a controlled interface. It features comprehensive logging, secure database access via environment variables, and proper error handling, making database exploration and analysis safer and more structured. The project is a Java conversion of a Python codebase, preserving core functionality while enabling seamless integration into Java-based environments.

The ConnectWise Manage API Gateway MCP Server facilitates seamless interaction with the ConnectWise Manage API by offering tools for API discovery, execution, and management. It features natural language search, categorized API navigation, and a fast memory system for efficient workflows. Designed for developers and AI assistants, it simplifies complex API interactions and enhances productivity.

The DALL-E MCP Server is a Model Context Protocol (MCP) server that integrates with OpenAI's DALL-E API to generate, edit, and create variations of images. It supports both DALL-E 2 and DALL-E 3 models, allowing users to create high-quality images based on text prompts. The server is designed to work seamlessly with Cline, ensuring generated images are saved and displayed correctly in the user's workspace.

This MCP server provides a comprehensive solution for managing third-party library documentation by scraping, processing, and indexing content. It uses semantic splitting, OpenAI embeddings, and SQLite for efficient storage and hybrid search (vector similarity and full-text search). The server supports versioning, enabling distinct storage and querying of different library versions. It includes tools for scraping, searching, listing libraries, and finding versions, along with a CLI for local management. Integration with Claude Desktop allows seamless usage in AI workflows.

The National Parks MCP Server integrates with the National Park Service (NPS) API to offer detailed information about U.S. National Parks. It provides tools to search for parks, get park details, check alerts, find visitor centers, locate campgrounds, and discover upcoming events. This server is designed to assist users in planning trips and accessing real-time park information efficiently.