LLMs by themselves are just black boxes capable of predicting the next token, leveraging patterns learned from vast datasets. This inherent capability allows them to generate coherent and contextually relevant text. However, their knowledge is fundamentally limited to the data they were trained on. # 「 Models are Only as Good as Their Context 」[^1] For years, the primary methods to imbue AI models with new or specialized knowledge have been through extensive training or subsequent fine-tuning. While these methods have their place, they come with significant drawbacks when dealing with the need for live, constantly changing information. Retraining from scratch is computationally expensive and time-consuming. Fine-tuning, while more efficient than full retraining, can still be costly and may lead to "catastrophic forgetting" of previously learned information. Moreover, fine-tuned models become static and require repeated updates as new data emerges. This makes them less ideal for scenarios demanding up-to-the-minute accuracy. This "knowledge cutoff" means that an AI might be an expert on yesterday's news but oblivious to today's critical updates. (References: Rafay Systems, "Is Fine-Tuning or Prompt Engineering the Right Approach for AI?"; Nebius, "AI model fine-tuning: what it is and why it matters") Retrieval Augmented Generation (RAG) has offered a step forward, allowing models to fetch information from external knowledge bases to inform their responses. This can improve accuracy and provide more current information than a model's static training data alone. However, RAG systems often rely on indexed, pre-processed data, which, while more current than a model's core training, may still not be truly "live." The challenge remains: how can AI access and utilize data that is changing by the second, directly from the services where it originates? (References: DataMotion, "RAG vs. Fine-Tuning: Why Real-Time AI Outperforms Static Training"; FinetuneDB, "Fine-tuning vs. RAG: Understanding the Difference") # 「 Protocols are most Adequate in Dealing with Open World Thermodynamics 」 *Fourth Law; as the game keeps getting more complicated, and there are always more different ways to play. (Venkatesh Rao, "Tempo")* Enter the Model Context Protocol (MCP). MCP is not just another way to feed static documents to an AI; it's an open standard designed to enable AI models to _interact directly and securely with live external tools, APIs, and data sources in real-time_. Think of MCP as a universal translator and a dynamic data pipe, allowing an AI to query a live inventory database, check the current status of a flight, or access the latest customer interaction notes from a CRM, all at the moment it's needed to answer a specific prompt. This protocol standardizes how AI applications (clients) communicate with these external data and tool providers (servers), making integrations more streamlined and robust. (References: Anthropic; OpenCV, "A beginners Guide on Model Context Protocol (MCP)"; DhiWise, "Understanding Model Context Protocols in AI Systems") ![[Writing MCPs versus Further Training or Specialized Agents 2025-05-14 14.10.24.excalidraw.png]] %%[[Writing MCPs versus Further Training or Specialized Agents 2025-05-14 14.10.24.excalidraw.md|🖋 Edit in Excalidraw]]%% The core advantage of MCPs lies in their ability to provide AI with _live, tailored data access_. When a user poses a question, an MCP-enabled AI doesn't just rely on its pre-existing knowledge. Instead, it can use MCP to formulate a request to the relevant live service, fetch the precise data needed to address the prompt, and then use that fresh, contextual information to generate a highly relevant and accurate response. Under the hood, Model Context Protocol (MCP) utilizes three fundamental building blocks: **Prompts**, **Resources**, and **Tools**. Prompts serve as the initial triggers for AI interactions, often initiated by user commands. They provide *structured guidance*, allowing users to invoke specific functionalities within the AI system. Resources expose external data to the AI, enriching its understanding and enabling *context retrieval*. These can include various formats like text documents and binary files. The application manages these resources, deciding when and how the AI can access them for relevant information. Tools empower AI models to perform actions in external systems, extending their capabilities beyond mere information retrieval. Defined by JSON schemas, tools allow the AI to *execute tasks* like making API calls. This enables the AI to interact dynamically with the real world based on user requests. These three primitives are designed to work in concert within a client-server architecture. A user's prompt initiates a workflow where the AI might need to access specific resources for context or utilize tools to carry out actions. The client manages the fetching of resources, while the AI can dynamically invoke tools to fulfill the user's request as defined by the initial prompt. ![[Writing MCPs versus Further Training or Specialized Agents 2025-05-14 14.47.57.excalidraw.png]] %%[[Writing MCPs versus Further Training or Specialized Agents 2025-05-14 14.47.57.excalidraw.md|🖋 Edit in Excalidraw]]%% This is a paradigm shift from trying to stuff all possible knowledge into a model, to enabling the model to intelligently seek out and use specific, current information on demand. (References: Hypotenuse AI, "What is Model Context Protocol and How Does It Benefit Ecommerce?"; Qatalog, "AI with access to real-time data") This brings us to a critical business consideration: the cost. Continuously retraining or fine-tuning AI models to keep pace with rapidly changing business data is a financially draining and operationally cumbersome cycle. Each update requires significant compute resources, data preparation, and expert oversight. MCPs offer a dramatically more cost-effective and agile alternative. Instead of frequent, large-scale model updates, businesses can invest in building or leveraging MCP integrations to their key data services. This allows a general-purpose AI model to access specialized, live data as needed, effectively gaining the benefits of a specialized model on a per-query basis without the associated high recurring costs of retraining. The initial setup for MCP integrations is typically far less than a single fine-tuning cycle for a large model, and the ongoing operational costs are focused on API calls and data transfer, which are often more predictable and scalable. (References: Reddit, "Fine tuning training cost 10,000 PDFs"; Rafay Systems, "Is Fine-Tuning or Prompt Engineering the Right Approach for AI?") Beyond the significant cost savings, MCPs unlock crucial operational advantages. **Agility** is paramount; new data sources or services can be integrated via MCPs without needing to overhaul the core AI model, allowing businesses to adapt quickly to new opportunities or changing market conditions. **Scalability** is enhanced because it’s often simpler to scale API access and data connectors than to scale the infrastructure and processes for continuous model retraining. Most importantly, **accuracy** and reliability see a substantial boost. By grounding AI responses in live, factual data retrieved directly from source systems, the propensity for AI models to "hallucinate" or provide outdated information is dramatically reduced. This is critical for building trust and making dependable, data-driven decisions. (References: Hypotenuse AI, "Scalability and Flexibility"; Aalpha Information Systems, "What is MCP in AI and Its Benefits"; DataMotion, "Real-Time Data Access & Compliance") The power of MCPs is further amplified by their nature as an **open standard**. Like HTTP for the web or USB for peripherals, a standardized protocol for AI-to-data interaction fosters a collaborative ecosystem. Developers can build MCP servers for their services, knowing that any MCP-compliant AI client can potentially interact with them [^2]. This reduces vendor lock-in, encourages innovation, and simplifies the creation of complex AI applications that draw information from diverse sources. This **interoperability** is key to building a more connected and intelligent AI landscape. (References: Digidop, "Model Context Protocol (MCP) - The Future of AI Integration"; OpenCV, "Standardization & Collaboration"; DataNorth AI, "Model Context Protocol (MCP)") [^1]: [Unpacking the AI App Store: OpenAI's GPTs vs. Anthropic's MCPs](https://www.aspacetoth.ink/p/unpacking-the-ai-app-store-openais) [^2]:[Agents vs MCPs: Is the AI Hype Shifting? - DEV Community](https://dev.to/bekahhw/agents-vs-mcps-is-the-ai-hype-shifting-4593)