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 Benchmark: Top MCP Servers for Web Access

Model Context ProtocolFeb 11

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. We also performed a load test involving 250 concurrent AI agents.

Model Context ProtocolJan 28

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.

Model Context ProtocolJan 27

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 ProtocolNov 26

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.