Tutorials

What AIbase wants to introduce to you is a popular MCP application - HowToCook - MCP Server. This application cleverly combines AI technology with food preparation and is committed to becoming your "personal chef", allowing you to enjoy different delicious meals every day.

OpenMemory MCP (Model Context Protocol) is officially launched, providing a unified local memory sharing solution for AI tools. This open-source tool allows users to store AI interaction content locally and share it via the MCP protocol to supported clients such as Claude, Cursor, and Windsurf. Cross-tool context synchronization can be achieved by maintaining just one copy of the memory content. Aibase has observed that the release of OpenMemory MCP has quickly sparked intense discussions among developers and is regarded as a significant innovation in improving the efficiency of AI workflows.

The agent of Nano AI has brought a revolutionary tool for English learners and educators. This tutorial will guide you in detail on how to use the agent of Nano AI to call the Model Context Protocol (MCP) to easily create a beautiful English learning material webpage that includes Chinese - English bilingual comparisons, core vocabulary analysis, and supporting MP3 audio, and directly generate a shareable link. This process will greatly improve your efficiency in creating personalized learning materials.

Bing MCP is not just a search tool; it's more like your personal intelligent assistant. By combining deep learning with Chinese semantics, it accurately understands users' search intentions and provides more expected results. Instead of a monotonous list of web pages, it's a "direct access channel" that allows you to find the information you need in the shortest time.

In the past few months, the AI circle has been suddenly flooded with discussions about MCP. This Model Context Protocol, launched by Anthropic at the end of 2024, is hailed as the "USB - C interface of the AI world". In simple terms, it's like a universal socket that allows large - scale models to easily connect to external tools, databases, and even physical devices, enabling a shift from just "talking" to "taking action".

Core concept: The capabilities of a model largely depend on the contextual information it receives. Evolution path: From the early days when chatbots required manual input of all context, we've progressed to models that can directly connect to data sources and tools, enabling more powerful and personalized AI applications. Objective of MCP: As an open protocol, it aims to achieve seamless integration between AI applications, agents, and various tools and data sources.

MCP provides several different "communication methods" (transmission mechanisms), among which three are the most notable: Stdio, SSE, and Streamable HTTP. They are like different means of transportation, each with its own advantages and disadvantages, and are suitable for different "road conditions" (application scenarios). Understanding their differences is the key to choosing the best "travel plan" for our AI applications.

The emergence of MCP marks that prompt engineering has entered a new stage of development. By providing more structured contextual information, it significantly enhances the capabilities of the model. When designing prompts, our goal is to integrate more specific information (such as local files, database contents, or real - time network data) to enable the model to better understand and solve real - world problems.

MCP is an open, general, and consensus-based protocol standard jointly released under the leadership of Anthropic (Claude). It's like installing a 'universal interface' for large AI models, enabling AI models to seamlessly interact with different data sources and tools.