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AI Agent Development Example with Custom MCP Server: Build A Code Review Agent – Part I Using MCP servers can make your AI agents intelligent and more rooted in the context of the task. This ensures that the LLM model gets right context about your task so that it can produce results specific to your goals. In this comprehensive guide, we'll demonstrate AI agent development by building a practical example: a code review agent that integrates with Claude Desktop using the Model Context Protocol (MCP). Through this hands-on MCP development tutorial, you'll learn how to create AI agents for software development that can automatically detect your project's programming language, load appropriate review checklists, and provide structured feedback. By the end of this guide, you'll understand the fundamentals of building AI agents and have a fully functional code review tool that you can customize for your team's specific needs or adapt for entirely different use cases. Explore more about MCP and its role in AI systems: Learn more about MCP and its role in AI systems here What we will build: A code review agent that works with Claude Desktop Automatic technology detection for Python, JavaScript, Java, Go, Rust, and TypeScript Customizable review checklists with security, quality, and performance checks Pattern-based code analysis using regular expressions Real-time progress tracking during reviews Time required: 30-45 minutes Skill level: Intermediate Python knowledge Prerequisites: Python 3.11 or higher installed Claude Desktop application Basic command line familiarity Understanding AI Agent Development Through a Code Review Implementation Before we dive in, let's clarify exactly how this code review system works: How the Code Review Process Works in Our AI Agent Our implementation uses static code analysis, not AI-based code review. Here's what happens: Pattern Matching: Devs create a YAML checklist containing regex patterns based on specific requirements. The system used this checklist to check flag errors line by line (e.g., eval(, hardcoded passwords, console.log). File Validation: Checks if required files exist (e.g., requirements.txt, package.json) Static Analysis: No actual code execution - just text pattern matching Discover more about AI solutions for businesses: Check out our AI services for businesses to explore custom AI solutions. The AI Agent's Role: How Claude Orchestrates MCP Tools Claude's role is limited to: Natural Language Interface: You can ask "review my Python code" instead of calling command-line tools Tool Orchestration: Claude decides which MCP server development tools to call based on your request Result Presentation: Claude formats and explains the findings in conversational language What This Means for AI Agent Development Regex patterns do the actual code review you define in YAML files Not AI-based: Claude doesn't analyze your code semantically or understand logic Pattern-based: You define what to look for (like "find all eval() calls") Customizable: You control exactly what gets checked by editing YAML checklists Deterministic: Same code always produces the same results (no AI variability) Why Building AI Agents with MCP This Way Is Effective ? This hybrid approach gives you: Control: You define the exact rules via YAML checklists Speed: Pattern matching is fast, no AI inference needed for scanning Consistency: Deterministic results every time Extensibility: Easy to add new checks without AI training Convenience: Natural language interface via Claude Desktop Think of it as linting rules + Claude's conversational interface. You're essentially building an AI agent with MCP that runs as a customizable linter through natural conversation. Read More: https://mobisoftinfotech.com/resources/blog/ai-development/ai-agent-development-custom-mcp-server-code-review

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Ethereum Smart Contract Development Guide: How to Build Secure Blockchain The automation, transparency, and trustless nature of transactions have redefined digital trust. As industries move toward decentralized ecosystems, smart contracts have become the backbone of this transformation. They eliminate intermediaries, ensure immutability, and execute actions based on predefined code. Within this framework, Ethereum blockchain development stands as the pioneering force shaping how trust functions in the digital economy. What are Smart Contracts and Their Core Mechanisms Smart contracts are self-executing programs on the blockchain that automatically facilitate transactions when predetermined conditions are met. They ensure transparency, accuracy, and prevent manipulation. Before Ethereum, blockchains merely stored transactional data, but Ethereum introduced a programmable layer that enabled decentralized applications (dApps) and autonomous financial protocols — a revolution in digital agreements. The Ethereum Blockchain Framework At the heart of Ethereum lies the Ethereum Virtual Machine (EVM), executing smart contracts across a global network of nodes with unmatched consistency and security. Ethereum’s native language, Solidity, empowers developers to build intricate decentralized architectures. Its syntax mirrors JavaScript but incorporates blockchain-specific functions like gas optimization, inheritance, and modifiers, making it the backbone of secure smart contract creation. Steps to Build Secure Smart Contracts Every smart contract begins with a defined purpose: automating payments, issuing NFTs, or managing supply chains. Developers code logic in Solidity, test it rigorously using frameworks like Truffle or Hardhat, and deploy it via Remix or Infura to the Ethereum mainnet. A structured process ensures reliability, performance, and efficiency. Best Practices for Blockchain Security Security begins with thorough code auditing, peer reviews, and automated vulnerability checks. Developers must avoid unchecked external calls, minimize on-chain storage, and adopt modular designs for easy upgrades. One minor flaw can jeopardize vast digital assets, making security a continuous discipline. Why Choose JustTry Technologies JustTry Technologies excels in Web3 smart contract development, merging precision with innovation. As a trusted smart contract development company, it specializes in Ethereum-based solutions designed for performance, compliance, and business alignment. Through rigorous testing and audits, JustTry Technologies ensures secure, scalable, and future-ready blockchain deployments that enhance transparency, automation, and trust. Visit: https://justtrytech.com/smart-contract-development-company/ WhatsApp: https://wa.me/919500139200 Email ID: sales@justtrytech.com #Smartcontractdevelopmentcompany #smartcontractdevelopmentservices #usa #uae #canada #blockchaindevelopmentinusa #web3smartcontractdevelopment

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Introduction to SEO-Friendly JSON-LD by Marcitors JSON-LD (JavaScript Object Notation for Linked Data) is the recommended format by Google for implementing structured data. It’s cleaner, easier to manage, and less intrusive than other methods, allowing you to separate your structured data from your main HTML content. https://www.slideshare.net/slideshow/introduction-to-seo-friendly-json-ld-by-marcitors/283669841

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Comparing Flutter and React Native? This guide explores their performance, UI flexibility, developer productivity, and future readiness. Flutter offers fast rendering and rich UI customization, while React Native benefits from a vast JavaScript ecosystem and quicker onboarding. Both frameworks support rapid development, but differ in architecture and long-term scalability. Ideal for CTOs and founders, this comparison helps you choose the right cross-platform tech to drive results, streamline development, and future-proof your app strategy. Make an informed decision with confidence. Make your move with the best framework: https://flutternest.com/blog/flutter-vs-reactnative-app-development