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.
MCP Security: Best Practices and Avoid Common Pitfalls
The model context protocol (MCP), pioneered by Anthropic, is quickly becoming the go-to standard for connecting large language models (LLMs) to the outside world. But the same simplicity that makes MCP so powerful also makes it risky.
MCP Benchmark: Top MCP Servers for Web Access
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.
How ACP Enables Interoperable Agent Communication?
We’re starting to see GenAI move toward standardization, similar to how HTTP transformed the internet in the early 1990s. Just as HTTP enabled the rise of the World Wide Web, new protocols are emerging.
Figma MCP Server Tested – Figma to Code
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.
Centralizing AI Tool Access with the MCP Gateway
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 Protocol (MCP) and Its Importance
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.