This article compares Model Context Protocol (MCP), Function Calling, and OpenAPI Tools for integrating tools and resources with language models, outlining their strengths, limits, security considerations, and ideal use cases.
This article discusses the concept of 'tool masking' as a way to optimize the interaction between LLMs and APIs, arguing that simply exposing all API functionality (as done by MCP) is inefficient and degrades performance. It proposes shaping the tool surface to match the specific use case, improving accuracy, cost, and latency.
The article explores how modern AI agents are fulfilling the vision of the Semantic Web by combining AI's learned intuition with the logical structure of semantic technologies, creating intelligent agents that can understand and act on behalf of users.
This blog post explains that Large Language Models (LLMs) don't need to understand the Model Context Protocol (MCP) to utilize tools. MCP standardizes tool calling, simplifying agent development for developers while the LLM simply generates tool call suggestions based on provided definitions. The article details tool calling, MCP's function, and how it relates to context engineering.
The Azure MCP Server implements the MCP specification to create a seamless connection between AI agents and Azure services. It allows agents to interact with various Azure services like AI Search, App Configuration, Cosmos DB, and more.
Take a deep dive into how the Model Context Protocol (MCP) works and solidify your understanding by building an agentic system that uses it.
This article details the Model Context Protocol (MCP), an open standard for connecting AI agents to tools and data across enterprise landscapes. It covers MCP implementations by AWS, Azure, and Google Cloud, security considerations, and the growing ecosystem surrounding the protocol.
The Universal Tool Calling Protocol (UTCP) is an open standard that describes how to call existing tools directly, eliminating the need for wrappers. It focuses on direct communication with tool endpoints (HTTP, gRPC, WebSocket, CLI, etc.) to reduce latency and maintain existing security and billing systems.
Leveraging MCP for automating your daily routine. This article explores the Model Context Protocol (MCP) and demonstrates how to build a toolkit for analysts using it, including creating a local MCP server with useful tools and integrating it with AI tools like Claude Desktop.
This article introduces MCP (Model Context Protocol) and its potential applications, particularly with Google Sheets. It explores what MCP is, addressing the ambiguity surrounding its definition and explaining its role as a standardized practice, protocol, and library for AI Agents.
