MCP Core Components and Engineering Best Practices

Step-by-Step Guide to Build an MCP-Powered Application Master every layer—from GenAI foundations to deployment—for building powerful, context-aware AI agents using the Model Context Protocol (MCP). Level 1: Foundations of GenAI Start by building your technical base. Learn how APIs work, understand the mechanics behind LLMs like GPT and Claude, and master prompt engineering basics such as role-based or chain-of-thought prompting. Get hands-on with GenAI toolchains like OpenAI function calls, Informatica iPaaS and LangChain to prep for advanced integration. Level 2: Deep Dive into MCP Core Concepts Build the backbone of your MCP architecture. Start by connecting to vector stores for memory retrieval, design a robust routing layer to select the right tools or models, and implement logic for managing context across sessions. Then, create modular tool plugins and grasp how MCP connects context, memory, and tools for smarter decision-making. Level 3: Agent Integration & Deployment Bring your agent to life. Implement agent-to-agent (A2A) communication, add logging and observability for reliability, and expose your agent via APIs or apps. Finally, deploy to cloud platforms with secure endpoints, and adopt a cycle of continuous improvement by collecting feedback, testing tools, and tracking success metrics.

Under the hood of MCP (Model Context Protocol) 🔌 Think of MCP as the USB-C for AI apps - a standard way for models to plug into tools and data without brittle glue code. What is MCP actually? A protocol that lets an AI client (your assistant/IDE/desktop app) discover and safely call capabilities exposed by servers (your filesystem, DB, calendars, internal APIs, etc.). Core building blocks: 🔹 Tools – callable functions with typed schemas (e.g., “create_ticket”, “query_db”). 🔹 Resources – referenced context you can read (files, rows, logs, URLs). 🔹 Prompts – reusable, parameterized templates exposed by the server. The request flow (simplified): 🔹Discover: client asks server what tools/resources/prompts exist. 🔹Plan: model decides which to use from schemas + context. 🔹Invoke: client calls the tool via JSON-RPC. Results can stream. 🔹Ground: outputs and resource reads feed the model’s context and UI. Why engineers should care: 🔹Standard interface → fewer one-off integrations. 🔹Safety & control → explicit capability exposure and consent. 🔹Composability → one client can talk to many servers; one server can serve many clients. Mental model: Client (assistant) ↔ MCP ↔ Servers (filesystem, DB, SaaS, internal APIs) Practical tips 🔹Keep tools small, idempotent, and well-typed. 🔹Validate inputs; rate-limit and auth at the server. Bottom line: MCP turns “LLM + ad-hoc glue” into LLM + protocol, unlocking safer, faster, and more reliable AI integrations. #MCP #AIInfra #Agents #LLM #DeveloperTools #APIs #SoftwareEngineering

𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: 𝗔𝗜'𝘀 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗟𝗶𝗻𝗸 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 I’ve been exploring the architecture for connecting LLMs to external tools and data sources, and the Model Context Protocol (MCP) stands out as a potential game-changer. MCP solves one of the most critical challenges in AI development—creating standardized connections between models and the resources they need. MCP’s client-server architecture transforms what would traditionally require N×M custom integrations into a more manageable N+M structure. This elegance isn’t just theoretical—it’s a practical solution to a real engineering problem. Why MCP matters for AI engineers: - Standardized connections between LLMs and tools/data - Security boundaries that maintain isolation between models and sensitive systems - Simplified integration with a consistent protocol layer for all external systems The core architecture follows a three-tier approach: - Hosts: AI applications that initiate connections (like Anthropic AI's Claude Desktop) - Clients: Protocol components maintaining 1:1 connections with servers - Servers: Lightweight connectors exposing capabilities through standardized interfaces Under the hood, MCP uses JSON-RPC 2.0 for structured message exchange, supporting multiple transport mechanisms including stdio for local processes and HTTP with Server-Sent Events for remote connections. Implementing MCP servers is straightforward: - Python developers can use FastMCP with decorator-based tool definitions - JavaScript/TypeScript developers have access to the MCP SDK with explicit request handlers - Both approaches use JSON Schema for input validation and documentation For teams building AI products, MCP provides significant development advantages: - Reduced complexity so engineers can focus on functionality - Modular design allowing independent component development - Enhanced security with local-first processing and granular permissions The real power shows when implementing practical workflows like GitHub integration, database access, or multi-tool orchestration patterns that combine multiple data sources into cohesive experiences. Production deployment is simplified with: - Containerization for consistent environments - Monitoring across server health, protocol interactions, and business metrics - Resource optimization tailored to server function (I/O, CPU, or memory intensive) MCP creates a foundation for building more capable AI applications without sacrificing security or scalability by standardizing how AI accesses external systems. What integration patterns are you finding most challenging when connecting LLMs to your existing systems and tools?

🔍 Demystifying MCP Architecture: From APIs to organized MCP tools As organizations build their Agentic AI platforms, I'm often asked about best practices for organizing APIs into MCP tools and servers. Here's a practical guide based on real-world patterns: 🛠️ Converting APIs to MCP Tools Don't convert every API! Focus on atomic, goal-oriented operations that agents can meaningfully use. Re-use data custodian APIs. 🏗️ MCP Tools vs Servers Organization Think of MCP tools as lego blocks and MCP servers as themed lego sets. Group tools into servers based on: • Business domain (Shopping Cart, Product Catalog) • Team ownership (Payment Team, Inventory Team) • Security boundaries (Customer Data, Internal Ops) 🎯 Real Example: E-commerce Agent Architecture Shopping Cart MCP Server: cart management tools Catalog MCP Server: product search & browse tools Promotions MCP Server: discount & offer tools Policy MCP Server: compliance & rules tools 🔀 Gateway Strategy Use AgentCore Gateway to unify your MCP servers based on: AI Agent domain & use case Organization business unit #AgentCore #MCP #APIDesign #AgenticAI #CloudArchitecture #AWS Want to learn more about building scalable agent architectures? Drop your questions below! 👇

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐥𝐥 𝐧𝐨𝐭 𝐬𝐜𝐚𝐥𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝 𝐢𝐟 𝐭𝐡𝐞𝐲 𝐜𝐚𝐧 𝐧𝐨𝐭 𝐭𝐚𝐥𝐤 𝐭𝐨 𝐭𝐨𝐨𝐥𝐬, 𝐀𝐏𝐈𝐬, 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐲. This is where MCP (Model Context Protocol) changes the game. Think of MCP as the “middleware” that lets AI Agents plug into anything databases, APIs, configs, files, or even other agents. But here is the kicker: it is not a one-size-fits-all model. 𝐓𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝟖 𝐂𝐨𝐫𝐞 𝐌𝐂𝐏 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰: --- 1. Analytics Data Access Pattern MCP connects AI agents to OLAP systems via tools, making large-scale analytics queries possible. Use case: Business intelligence, dashboards, and real-time insights. 2. Configuration Use Pattern AI agents fetch and apply configurations directly from config management services. Use case: Dynamic system tuning, feature flagging, multi-tenant app setups. 3. Hierarchical MCP Pattern Parent MCP servers orchestrate domain-level MCPs (payments, wallet, customer). Use case: Enterprise architectures where domains must stay modular but interoperable. 4. Local Resource Access Pattern Agents execute file operations (read, write, transform) through MCP tools. Use case: Enterprise workflows with on-premise or hybrid file processing. 5. Event-Driven Integration Pattern MCP streams events into async workflows for real-time decisioning. Use case: Fraud detection, IoT alerts, trading signals, ops monitoring. 6. MCP-to-Agent Pattern General AI agents delegate tasks to specialist agents via MCP. Use case: Connecting a customer service bot to a finance-specific expert agent. 7. Direct API Wrapper Pattern MCP tools wrap APIs, making complex API integrations simpler and uniform. Use case: AI agents querying multiple SaaS tools (CRM, HR, billing) in one flow. 8. Composite Service Pattern MCP orchestrates multiple APIs into one unified service layer. Use case: Multi-step workflows like booking + payments + notifications. --- 👉 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: 𝐊𝐧𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞𝐬𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐭𝐨𝐲 𝐝𝐞𝐦𝐨 𝐚𝐧𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐌𝐂𝐏 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝟏𝟐 𝐦𝐨𝐧𝐭𝐡𝐬? ♻️ Repost this to help your network get started ➕ Follow Jothi Moorthy for more #AI #MCP #AIagents #SystemDesign

Things You Need to Know About MCP (Model Context Protocol) If you're building with AI, or even thinking about it, MCP is something you can't afford to ignore. • What is MCP? MCP is a standardized protocol that lets AI models interact with external tools, databases, and APIs in real time. Imagine ChatGPT or Claude being able to access your calendar, SQL database, or project management board on demand, that’s MCP in action. • Why MCP Matters? Most LLMs are frozen in time, trained on static data. But real-world tasks require live information. MCP breaks that boundary. It gives models eyes and ears into the current state of the world, allowing for contextual, timely, and accurate responses. • No More Custom Glue Code Before MCP, every integration was a snowflake. Connecting an AI to Google Calendar or a finance API meant writing custom code, again and again. MCP introduces a universal interface, one protocol. Infinite integrations. Scalable by design. • The Core Trio: Client, Protocol, Server MCP follows a modular design that comprises three primary components: a) MCP Client- The AI assistant or IDE that requests data or actions (e.g., Claude MCP, GoCodeo, VS Code IDE). b) MCP Protocol- The standardized framework that ensures consistent communication between clients and servers. c) MCP Server- The data handler that retrieves information from various data sources such as SQL databases, documents, or APIs. • Self-Describing Servers = Built-In Documentation Every MCP Server can describe its own capabilities. That means no digging through API docs or manually updating clients. The AI agent asks the server what it can do, and adjusts in real time. That’s dynamic adaptability, built-in. • Real-Time Bi-Directional Sync MCP doesn’t stop at request-response. Unlike traditional request-response models, MCP supports bi-directional communication, allowing MCP Servers to push updates back to clients without waiting for a new request. For example, if new calendar entries are added or updated in a monitored database, the MCP Server can proactively notify the client, ensuring real-time synchronization. • Built for Change, Designed for Scale Add a new data source? Modify an API? The client doesn’t break. Because of its modular and self-describing nature, MCP is inherently resilient to change. This makes it a perfect fit for enterprise-grade AI agents that must evolve fast. • MCP Is More Than a Protocol. It’s an AI Philosophy. It’s a shift from "AI as a frozen oracle" → to "AI as an active collaborator." With MCP, we stop treating models like black boxes, and start giving them the context, access, and agency they need to truly assist. If you believe the future of AI is agentic, dynamic, and deeply integrated, then MCP is the blueprint.

Model Context Protocol (MCP), clearly explained! (+9 hands-on MCP projects with code) Today, I’ll clearly explain what the Model Context Protocol (MCP) is — followed by 9 hands-on projects to make it real. 𝗦𝗶𝗺𝗽𝗹𝘆 𝗽𝘂𝘁, 𝗠𝗖𝗣 𝗶𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗨𝗦𝗕-𝗖 𝗽𝗼𝗿𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗟𝗟𝗠/𝗔𝗜 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. Just as USB-C offers a standardized way to connect devices to various accessories, MCP standardizes how your AI apps connect to different data sources and tools. At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. 𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗿𝗲𝗲 𝗸𝗲𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀: - Host - Client - Server Let's understand them one-by-one. 1️⃣ 𝗧𝗵𝗲 𝗛𝗼𝘀𝘁 Host: An AI app (Claude desktop, Cursor) that provides an environment for AI interactions, accesses tools and data, and runs the MCP Client. 2️⃣ 𝗧𝗵𝗲 𝗖𝗹𝗶𝗲𝗻𝘁 MCP Client: Operates within the host to enable communication with MCP servers. 3️⃣ 𝗧𝗵𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 A server exposes specific capabilities and provides access to data. There are 3 key abilities that the server exposes: - Tools: Enable LLMs to perform actions through your server - Resources: Expose data and content from your servers to LLMs - Prompts: Create reusable prompt templates and workflows For eg. a weather API server provides available `tools` to call API endpoints, `prompts`, and API documentation as `resource`. 🔷 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗰𝗹𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗲𝗿𝘃𝗲𝗿 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 ❓ Understanding client-server communication is essential for building your own MCP client-server. First of all, an exchange of capability happens where the client sends an initialize request to learn the server's capabilities and the server responds with its capability details. Then the client acknowledges a successful connection, and further message exchange continues. ↔️ 𝗧𝗵𝗲 𝘁𝘄𝗼-𝘄𝗮𝘆 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗠𝗖𝗣 Unlike traditional APIs, the MCP client-server communication is two-way. MCP client offers a capability called sampling, it allows servers to leverage clients' AI capabilities without requiring API keys While client maintains control over model access and permissions 🤔 𝗦𝗼 𝘄𝗵𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗱𝗼𝗲𝘀 𝗠𝗖𝗣 𝘀𝗼𝗹𝘃𝗲❓ Suppose you have M apps and N tools! Before MCP: - Every LLM/AI app operated in silos, each doing its own thing - Every new connection meant building a custom integration - M apps × N tools = M×N integrations - There was no shared protocol for engineers to rely on After MCP: - Just create one MCP server for your tool - It plugs into any AI app that speaks MCP - You go from M×N complexity to just M + N integrations _____ Now that you understand what MCP is, let’s make it concrete with 9 hands-on projects we've built:(link in the comments) _____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!