Building Modular, Compliant RAG Systems with MCP

MCP-Powered Agentic RAG Architecture represents a new way to combine intelligence, context, and adaptability in AI-driven systems. 
 At the heart of this architecture is a seamless flow between multiple components each playing a vital role in transforming a user’s question into a context-rich, accurate answer.
 It all begins with the user, who interacts through a front-end interface. Their query is passed to the MCP Client, which acts as the bridge between the user and the backend. The MCP Client sends the request, receives the response, and delivers a smooth experience.
 Behind the scenes, the MCP Server functions as the agent or the “thinking brain.” Rather than responding solely based on pre-trained knowledge, it evaluates whether it needs additional data to provide a better response. If so, it smartly leverages external tools to gather context.
 Two main tools make this possible: - The Vector Database Search Tool, designed to retrieve semantically similar information from private knowledge bases or internal documentation—this forms the retrieval layer of RAG. - The Web Search Tool, which supplements private data with up-to-date, relevant insights from across the internet when needed.
 By orchestrating these components, the MCP framework enables an adaptive, agentic RAG pipeline that combines private intelligence with live data for more accurate, reliable, and context-aware outputs. #MCP #AgenticAI #RAGArchitecture #AIEngineering #RetrievalAugmentedGeneration #VectorDB #AIAgents #LLMApplications #AIIntegration #ContextAwareAI

🚀 𝗗𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗖𝗣: 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗮𝗻𝗱 𝗧𝗵𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 The Model Context Protocol (MCP) architecture enables AI agents to use external tools securely and reliably. It operates on a foundation of three core parts and a precise, multi-step communication loop. Ⅰ. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝗠𝗖𝗣 The architecture separates responsibilities for scalability and security: ✨ 𝗔. 𝗧𝗵𝗲 𝗛𝗼𝘀𝘁 (𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻) The chatbot or agent that decides which tool is needed but does not handle the low-level communication. 🧠 🛠️ 𝗕. 𝗧𝗵𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 (𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗧𝗼𝗼𝗹) The service (e.g., GitHub, Slack, Drive) that executes the actual task. 🤝 𝗖. 𝗧𝗵𝗲 𝗠𝗖𝗣 𝗖𝗹𝗶𝗲𝗻𝘁 (𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗼𝗿) The dedicated middle layer. Crucially, each Server requires its own Client (a one-to-one relationship) to keep communication channels decoupled. 🔗 Ⅱ. 𝗧𝗵𝗲 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 Here is how a request (e.g., "Check for new commits") travels through the system: * User to LLM: The User asks the Host. The Host sends the prompt to the LLM. 🗣️ * LLM Decision: The LLM realizes the answer requires an external Server (GitHub). 🧭 * Host to Client: The Host sends a high-level request to the designated MCP Client. ➡️ * Client Translation: The Client converts the request into the JSON-RPC language for the Server. ✍️ * Execution: The Client sends the structured request to the Server (GitHub), which performs the task. ✅ * Server Response: The Server returns a structured MCP response to the Client. ↩️ * Client Interpretation: The Client translates the Server's structured data back into a format the Host and LLM can understand. 📖 * Final Answer: The LLM uses the data to generate the final answer for the User. ✨ This architecture ensures the LLM focuses only on reasoning, while the Client manages all the complex tool-specific communication. What external tool in your workflow would benefit most from this standardized approach? 👇 #AIArchitecture #MCP #AIAgents #LLMs #TechDeepDive

🔍 MCP: Model Context Protocol or Just a Rebranded Repository Strategy? Lately, I’ve seen a surge of excitement around “Model Context Protocols” (MCPs) in agentic AI circles—pitched as revolutionary abstractions for tool orchestration, context management, and modular reasoning. But let’s pause for a moment. Is MCP truly novel? Or is it just a remix of time-tested design patterns we’ve used for decades? 🧠 Repository Pattern: Abstracts data access from business logic. 🧠 Strategy Pattern: Swaps behaviors at runtime—sound familiar? 🧠 Abstract Factory: Produces families of interchangeable components. 🧠 DAO Layer: Encapsulates persistence logic, DB-agnostic by design. MCPs claim to decouple model logic from execution context. But isn’t that what we’ve always done with layered architectures, dependency injection, and interface-driven design? “Context-aware orchestration”? ✅ Already solved via behavioral patterns and service locators. “Tool-agnostic execution”? ✅ That’s Strategy + Factory + Adapter. “Modular reasoning pipelines”? ✅ Welcome back, Chain of Responsibility. Let’s not confuse terminology with innovation. Naming something doesn’t make it new. 💡 True progress lies in implementation nuance, not just semantic novelty. For those unfamiliar, these are foundational design patterns from the Gang of Four (GoF) that have shaped software architecture for decades. MCP may be useful—but let’s recognize it as an evolution, not a revolution. #DesignPatterns #AgenticAI #SoftwareArchitecture #OldWineNewBottle #MCP #RepositoryPattern #StrategyPattern #LinkedInBrickbat

Building even a simple 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗯𝗮𝘀𝗲𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 is a challenging task. Read until the end to understand why 👇 Here are some of the moving parts in the RAG based systems that you will need to take care of and continuously tune in order to achieve desired results: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: 𝘍 ) Chunking - how do you chunk the data that you will use for external context. - Small, Large chunks. - Sliding or tumbling window for chunking. - Retrieve parent or linked chunks when searching or just use originally retrieved data. 𝘊 ) Choosing the embedding model to embed and query and external context to/from the latent space. Considering Contextual embeddings. 𝘋 ) Vector Database. - Which Database to choose. - Where to host. - What metadata to store together with embeddings. - Indexing strategy. 𝘌 ) Vector Search - Choice of similarity measure. - Choosing the query path - metadata first vs. ANN first. - Hybrid search. 𝘎 ) Heuristics - business rules applied to your retrieval procedure. - Time importance. - Reranking. - Duplicate context (diversity ranking). - Source retrieval. - Conditional document preprocessing. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: 𝘈 ) LLM - Choosing the right Large Language Model to power your application. ✅ It is becoming less of a headache the further we are into the LLM craze. The performance of available LLMs are converging, both open source and proprietary. The main choice nowadays is around using a proprietary model or self-hosting. 𝘉 ) Prompt Engineering - having context available for usage in your prompts does not free you from the hard work of engineering the prompts. You will still need to align the system to produce outputs that you desire and prevent jailbreak scenarios. And let’s not forget the less popular part: 𝘏) Observing, Evaluating, Monitoring and Securing your application in production! What other pieces of the system am I missing? Let me know in the comments 👇

The MCP Prompts feature is a standardized mechanism within Anthropic’s Model Context Protocol (MCP) for creating and reusing structured prompt templates. Core Function: Defined in JSON format, MCP Prompts act as "recipes" that tell an LLM (like Claude) exactly how to process dynamic input data and what structured output to return. This standardizes how LLMs interact with external tools and servers (e.g., for data extraction, classification, or code review). Mechanism: Creation: A prompt template is defined with a unique ID, version, instructions, and placeholders (e.g., {input_text}). Invocation: An MCP server or client calls the prompt ID, supplying the necessary dynamic inputs. Execution: The LLM runs the standardized prompt (often integrated with MCP Sampling) and generates a consistent, structured output (e.g., JSON). Key Benefits: The feature ensures consistency and efficiency by eliminating repetitive manual prompt engineering. It offers traceability (via version IDs) and enables seamless interoperability across different MCP-compatible systems, automating complex, LLM-driven workflows. Use Case: Automatic Ticket Classification A customer service platform (MCP Server) receives a new support ticket. Instead of routing it manually, the server invokes a stored MCP Prompt named prompt/ticket-classifier, supplying the ticket text {ticket_text} as input. The LLM processes the standard prompt instructions (e.g., "Classify this text as 'Bug', 'Feature Request', or 'Question'") and returns a structured JSON output (e.g., {"type": "Bug", "urgency": "High"}). This classification is instant and consistent, allowing the server to automatically route the ticket to the correct queue. Reference: For technical specifications and documentation on the Model Context Protocol (MCP), consult Anthropic's official GitHub repository or developer documentation pages related to Claude. (Specific URL requires knowledge of Anthropic's internal documentation structure; search for "Anthropic Model Context Protocol" or "Claude MCP".)

𝗕𝘆 𝗺𝗶𝗱 𝟮𝟬𝟮𝟱, 𝗼𝘃𝗲𝗿 𝟱𝟭% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘂𝘀𝗲 𝗥𝗔𝗚 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 — 𝘂𝗽 𝗳𝗿𝗼𝗺 𝟯𝟭% 𝗷𝘂𝘀𝘁 𝗮 𝘆𝗲𝗮𝗿 𝗲𝗮𝗿𝗹𝗶𝗲𝗿. But here’s the real shift no one can ignore: → Traditional RAG is a one-shot search. → Agentic RAG is an ongoing conversation. And static retrieval isn’t enough anymore — not for real-world workflows, not at scale. That’s why it’s critical to understand the difference: ⬇️ 1. 𝙏𝙧𝙖𝙙𝙞𝙩𝙞𝙤𝙣𝙖𝙡 𝙍𝘼𝙂 → You input a query, and the system encodes it into a vector. → It searches a fixed vector database for the most similar documents. → The top-k documents are passed to the language model to generate an answer. → The model responds without checking if the documents were actually useful or relevant. → There’s no mechanism to revise, re-ask, or adapt based on the quality of the result. This works for simple, structured questions — but it fails when queries are vague, multi-step, or context-heavy. It’s a one-shot process with no memory or logic. 2. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → An agent first rewrites or decomposes the query to clarify intent or break it into steps. → It evaluates whether enough information has been retrieved — and if not, loops again. → It selects the right tool or source based on query type: database, live web, API, internal system. → Agents can validate partial answers, request more context, or reroute based on quality. → The final output is built through multiple reasoning steps — not just a single vector lookup. This creates a responsive, adaptive pipeline that mirrors how a human researcher would approach complex tasks. It's not just about retrieval — it's about reaching a trusted, verified result. 𝗪𝗵𝘆 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 > 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗔𝗚 → Smarter query handling → Multi-source selection → Autonomous decision flow → Built-in feedback loop → Composable and scalable → Lower hallucination risk → Customizable guardrails Traditional RAG retrieves. Agentic RAG reasons, retrieves, verifies, and responds. That's a major difference!

𝗕𝘆 𝗺𝗶𝗱 𝟮𝟬𝟮𝟱, 𝗼𝘃𝗲𝗿 𝟱𝟭% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘂𝘀𝗲 𝗥𝗔𝗚 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 — 𝘂𝗽 𝗳𝗿𝗼𝗺 𝟯𝟭% 𝗷𝘂𝘀𝘁 𝗮 𝘆𝗲𝗮𝗿 𝗲𝗮𝗿𝗹𝗶𝗲𝗿. But here’s the real shift no one can ignore: → Traditional RAG is a one-shot search. → Agentic RAG is an ongoing conversation. And static retrieval isn’t enough anymore — not for real-world workflows, not at scale. That’s why it’s critical to understand the difference: ⬇️ 1. 𝙏𝙧𝙖𝙙𝙞𝙩𝙞𝙤𝙣𝙖𝙡 𝙍𝘼𝙂 → You input a query, and the system encodes it into a vector. → It searches a fixed vector database for the most similar documents. → The top-k documents are passed to the language model to generate an answer. → The model responds without checking if the documents were actually useful or relevant. → There’s no mechanism to revise, re-ask, or adapt based on the quality of the result. This works for simple, structured questions — but it fails when queries are vague, multi-step, or context-heavy. It’s a one-shot process with no memory or logic. 2. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → An agent first rewrites or decomposes the query to clarify intent or break it into steps. → It evaluates whether enough information has been retrieved — and if not, loops again. → It selects the right tool or source based on query type: database, live web, API, internal system. → Agents can validate partial answers, request more context, or reroute based on quality. → The final output is built through multiple reasoning steps — not just a single vector lookup. This creates a responsive, adaptive pipeline that mirrors how a human researcher would approach complex tasks. It's not just about retrieval — it's about reaching a trusted, verified result. 𝗪𝗵𝘆 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 > 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗔𝗚 → Smarter query handling → Multi-source selection → Autonomous decision flow → Built-in feedback loop → Composable and scalable → Lower hallucination risk → Customizable guardrails Traditional RAG retrieves. Agentic RAG reasons, retrieves, verifies, and responds. That's a major difference!

Core Architectural Components of the Model Context Protocol (MCP) The Model Context Protocol (MCP) brings a client-server model inspired by the Language Server Protocol (LSP) — offering a more modular and extensible approach to AI tool integration. Here’s how it works 👇 🔹 MCP Host The Host is the main application that manages multiple MCP clients. It handles user experience, security enforcement, and orchestration of tools — ensuring safe and seamless AI interactions. 🔹 MCP Client The Client acts as the bridge between the Host and the Server. It manages communication sessions, sends commands, and receives responses — keeping the workflow efficient and connected. 🔹 MCP Server The Server is where the magic happens — it provides the capabilities and tools that AI applications use. It handles tool discovery, command execution, and result formatting, often acting as a gateway to external APIs or data sources. In enterprise environments, the Server also plays a key role in ensuring security, scalability, and governance. 💡 The MCP’s architecture empowers developers to build flexible, secure, and tool-agnostic AI ecosystems, paving the way for the next generation of intelligent systems. #AI #Architecture #ModelContextProtocol #Innovation #TechDesign #OpenAI #LSP #SoftwareDevelopment

Enterprise RAG Architecture  What it is Enterprise RAG (Retrieval-Augmented Generation) enriches an LLM with authoritative knowledge from internal or trusted data sources to ensure accuracy, compliance, and traceability. Why it’s important Reduces hallucinations Ensures knowledge stays current Enables governance + audit trails Improves reliability in high-risk domains (Finance, Healthcare, Gov) How the System Works:  1- User Query User sends a question or task → system prepares it for retrieval and generation.  2- Preprocessing & Embedding Query converted into vector form for similarity search. 3- Vector Search + Re-Ranking Relevant chunks retrieved from knowledge base → scored to ensure best match. 4- Context Validation If confidence high → proceed with retrieved info If confidence low → fallback LLM / ask user for details 5- Augmented Prompt Construction User query combined with retrieved evidence → structured input for LLM. 6- Hallucination Filter Trust + compliance checks before finalization. 7- LLM Generation with Citations Response grounded in original sources → transparency and auditability. 8- Feedback + Data Improvement Loop System logs results and updates vector store → gets smarter over time. #RAG #AI

🚀 Day 111 of 180 — Exploring Low-Level Design (LLD) of URL Shortener System Today’s learning focused on the Low-Level Design (LLD) of URL shortener systems — understanding how long URLs are transformed into short, unique identifiers efficiently and safely. 🔐 URL Encoding Techniques To generate short, unique URLs, we use hashing or encoding algorithms like Base62, MD5, or a Counter-based approach. 1️⃣ Base62 Encoding Uses A–Z, a–z, 0–9 (62 total characters). Supports ~3.5 trillion (62⁷) unique combinations. Scalable and random, but prone to collisions in multi-server setups if not synchronized. 2️⃣ MD5 Encoding MD5 generates a 128-bit hash. Only first 43 bits are used for a 7-character URL. Requires a collision check in the database to prevent data corruption. Advantage: same long URL → same short URL → deduplication → less storage use. 3️⃣ Counter-Based Approach A distributed counter generates unique incremental IDs for every request. Ensures no collisions across multiple servers. Ideal for scalable systems when combined with Base62 encoding to convert numeric IDs into short strings. 🧠 Database Storage & Concurrency Handling When shortening URLs, we store mappings like: { short_url → long_url, created_at, expiration_time } Challenges in distributed systems: Race conditions if multiple servers generate the same short code simultaneously. Solved via centralized counters, unique key constraints, or distributed locks. 💡 Reflection This session helped me understand how something as simple as a short link relies on hashing algorithms, data consistency models, and concurrency control. It’s a perfect example of low-level design thinking — balancing simplicity, performance, and scalability. Grateful to algorithms365 and Mahesh Arali Sir for the continuous mentorship in developing structured system design thinking. #Day111 #SystemDesign #LowLevelDesign #URLShortener #Hashing #Base62 #MD5 #Concurrency #Scalability #BackendEngineering #Algorithms365 #LearningJourney