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 Model Context Protocol (MCP), an open standard designed to connect AI agents with tools and data. It details the key components of MCP, its benefits (improved interoperability, future-proofing, and modularity), and its adoption in open-source agent frameworks like LangChain, CrewAI, and AutoGen. It also includes case studies of MCP implementation at Block and in developer tools.
This document details the features, best practices, and migration guidance for GPT-5, OpenAI's most intelligent model. It covers new API features like minimal reasoning effort, verbosity control, custom tools, and allowed tools, along with prompting guidance and migration strategies from older models and APIs.
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.
A detailed blog post discussing OpenAI's newly released open-weight GPT models, including performance benchmarks, initial testing on various hardware (Mac laptops, Cerebras), and comparisons to other open-source models. It covers aspects like reasoning capabilities, tool calling, and the new OpenAI Harmony prompt format.
The official Python SDK for Model Context Protocol servers and clients. It allows building MCP clients, servers, and provides tools for interacting with LLMs in a standardized way.
An MCP server that gives language models temporal awareness and time calculation abilities. Teaching AI the significance of the passage of time through collaborative tool development.
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.
An overview of the top AI-powered coding tools available in 2025, including Cursor, Claude Code, Windsurf, VSCode, and others, highlighting their features and popularity among developers.
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
Mistral Small 3.2 is a minor update to the Mistral Small 3.1 model, offering improvements in instruction following, repetition errors, and function calling. The article details the author's experience running the model locally using Ollama and GGUF quantizations, including generating an SVG image and describing it with the model itself.
