This project provides a lightweight, zero-burden MCP server designed to interact with PostgreSQL databases. It supports CRUD operations, schema management, and automation tools, eliminating the need for Node.js or Python environments. The server includes features like read-only mode, query plan checking, and SSE (Server-Sent Events) support, making it a versatile solution for database integration and automation tasks.

The Deep Research MCP Server is an agent-based tool designed to provide advanced research capabilities, including web search, PDF and document analysis, image analysis, YouTube transcript retrieval, and archive site search. It leverages HuggingFace's smolagents and requires Python 3.11 or higher, along with API keys for OpenAI, HuggingFace, and SerpAPI. The server is implemented as an MCP server, offering a robust solution for complex research tasks.

This MCP server facilitates seamless integration between Claude Desktop and Oracle databases, allowing users to query and interact with Oracle data directly from Claude. It supports Python 3.12+ and includes configuration options for both MacOS and Windows. The server is designed to enhance Claude's capabilities by providing direct access to Oracle database resources.

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 iFlytek Workflow MCP Server is a Python-based implementation that enables seamless integration of iFlytek workflows with the Model Context Protocol (MCP). It supports intelligent workflow scheduling, robust node support, advanced orchestration modes, and multiple development paradigms. This server is designed to enhance automation and flexibility in various business scenarios by leveraging the MCP framework.

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.

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.

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 project provides a thin wrapper around the OpenPyXl Python library, exposing its features as a Model Context Protocol (MCP) server. It allows MCP clients like Claude to interact with and extract data from Excel files. The server supports integration with Claude Desktop and other MCP-enabled tools, enabling users to query Excel data directly within their workflows. It simplifies data analysis by automating Excel file interactions through MCP.

The WhatTimeIsIt MCP Server is a lightweight implementation of the Model Context Protocol (MCP) designed to return the current time in ISO 8601 format based on the user's IP address. It integrates with the World Time API to fetch accurate time data and is built using Python. This server is ideal for applications requiring precise time synchronization based on the user's location.

The Fetch MCP Server is designed to retrieve and process web content, even from pages requiring JavaScript rendering or using anti-scraping techniques. It leverages browser automation, OCR, and multiple extraction methods to ensure high-quality content retrieval. The server includes a sophisticated scoring system to select the best results, making it ideal for integrating with LLMs for web content processing.

The Chargebee MCP Server provides tools to manage context between large language models (LLMs) and external systems. It offers context-aware code snippets, access to Chargebee's knowledge base, and seamless integration with AI-powered code editors such as Cursor, Windsurf, and Claude Desktop. This server helps developers get immediate answers about Chargebee products and API services, streamlining integration workflows.

The OceanBase MCP Server facilitates secure and efficient communication with OceanBase databases using the Model Context Protocol (MCP). It provides a robust interface for managing database interactions, ensuring data integrity and security. This server is designed to support various tools and applications that rely on OceanBase, making it a critical component for database management and integration.

WebSearch-MCP is a Model Context Protocol server designed to enable real-time web search functionality for AI assistants such as Claude. It integrates with a WebSearch Crawler API to retrieve up-to-date search results, allowing AI models to access current information on any topic. The server supports configuration via environment variables and can be easily integrated with various MCP clients.

The Model Context Protocol TypeScript SDK provides a comprehensive implementation of the MCP specification, enabling developers to build MCP servers and clients efficiently. It supports features like resources, tools, and prompts, and offers standard transports such as stdio and SSE. This SDK simplifies the process of integrating context-aware functionality into LLM applications, ensuring secure and standardized interactions.

This project enables the exposure of a local MCP server using Flask, allowing for seamless integration with platforms such as Coze, Dify, and FastGPT. It facilitates the interaction between these platforms and local resources through a structured API, making it easier to manage and utilize local services in a cloud-based environment.

The Travel Planning MCP Server is designed to assist with complex travel planning by integrating with APIs like Booking.com and Google Maps. It supports functions such as searching for flights, finding hotels, and providing the current date for temporal context. Future features include car rentals, hotel reviews, and taxi services. It can be used programmatically or with Claude Desktop for seamless travel planning.