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.
Code Execution with MCP: A New Approach to AI Agent Efficiency
Anthropic introduced a method in which AI agents interact with Model Context Protocol (MCP) servers by writing executable code rather than making direct calls to tools. The agent treats tools as files on a computer, finds what it needs, and uses them directly with code, so intermediate data doesn’t have to pass through the model’s memory.
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.
AI Apps with MCP Memory Benchmark & Tutorial
We tested memory tools to find out which ones actually work best for AI agents. Using LangChain’s ReAct Agent, we connected four different Model Context Protocol (MCP) memory servers and measured their performance in real scenarios. Beyond benchmarking, we built a working demo that connects Claude with Cursor through OpenMemory MCP.
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.