Model Context Protocol

Model Context Protocol (MCP) is an open standard that lets AI models connect to external data sources and tools through a unified interface. We benchmark and compare various MCPs to evaluate their performance, reliability, and capabilities.

How to Connect Your AI Apps with MCP Memory [2025]

Model Context ProtocolAug 15

In this tutorial, we’ll explore how to integrate Claude with Cursor to implement context-aware shared memory with OpenMemory MCP. This integration will allow us to demonstrate how memory is retrieved and managed in real-time. We’ll also dive into testing MCP’s ability to differentiate between various projects.

Model Context ProtocolAug 15

Centralizing AI Tool Access with the MCP Gateway in 2025

Source: Jahgirdar, Manoj In this article, I’ll walk through the evolution of AI tool integration, explain what the Model Context Protocol (MCP) is, and show why MCP alone isn’t production-ready. Then we’ll explore real-world gateway implementations between AI agents and external tools.

Model Context ProtocolAug 15

Figma MCP Server Tested – Figma to Code in 2025

Figma has announced the beta release of its Dev Mode MCP server, which connects design files directly to AI coding tools like Cursor, Windsurf, and Claude Code. The server uses Model Context Protocol (MCP) to provide design context to Large Language Models, enabling code generation that reflects both design specifications and existing codebase patterns.

Model Context ProtocolAug 15

MCP Benchmark: Top MCP Servers for Web Access in 2025

MCP (Model Context Protocol) establishes a standardized communication bridge between AI agents and applications, allowing AI apps and LLMs to interact with external tools and services. We benchmarked 8 MCP servers across web search and extraction, as well as browser automation tasks, by running 4 different tasks 5 times on all suitable MCPs.

Model Context ProtocolAug 15

Model Context Protocol (MCP) and Its Importance in 2025

Model Context Protocol (MCP) is an open protocol that standardizes how applications, databases and tools provide context to LLMs. More simply, it enables applications to connect to AI models, helping to achieve standardized results. Why is it important? MCP servers are becoming more popular because of their integration capabilities with AI systems.