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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Product-menu-thumb.jpg)](https://go.gigaspaces.com/webinar/why-conventional-rag-falls-short)

Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Proactive AI Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/How-it-works-menu.jpg)](https://go.gigaspaces.com/effective-ai-governance-wp)

Ensure GenAI compliance and governance 

[Read the Whitepaper![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/effective-ai-governance-wp)

  * [Success Stories![](https://www.gigaspaces.com/wp-content/uploads/2022/11/Path-16484.svg)](https://www.gigaspaces.com/case-studies)

    * By Use Case

      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
    * By Industry

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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Company-menu-thumb.jpg)](https://go.gigaspaces.com/webinarod/ecurely-unlocking-structured-data-for-enterprise-ai-0)

Monkey See, AI Do - All about CUA

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Taking the AI leap from RAG to TAG

[Read the Blog![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/blog/from-rag-to-tag)

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GigaSpaces, IBM & AWS make AI safer

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  * Products
    * Our Products
      * [eRAG](https://www.gigaspaces.com/products/erag)
        * [GenAI Catalyst](https://www.gigaspaces.com/products/genai-catalyst)
        * [Instant Data](https://www.gigaspaces.com/products/instant-data)
        * [Respond Proactively](https://www.gigaspaces.com/products/proactive-ai)
        * [Act Autonomously](https://www.gigaspaces.com/products/data-autonomy)
      * [Smart DIH](https://www.gigaspaces.com/products/smart-digital-integration-hub)
      * [XAP](https://www.gigaspaces.com/products/xap-imdg)
    * Solutions for
      * [Pharma](https://www.gigaspaces.com/products/erag/pharma)
      * [Procurement](https://www.gigaspaces.com/products/erag/procurement)
  * Solutions
    * [Digital Innovation Over Legacy Systems](https://www.gigaspaces.com/use-cases/digital-innovation-acceleration)
    * [Integration Data Hub](https://www.gigaspaces.com/technology/integration-data-hub)
    * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
    * [Hybrid/Multi-cloud Integration](https://www.gigaspaces.com/use-cases/hybrid-or-multi-cloud-integration)
    * [Customer 360](https://www.gigaspaces.com/use-cases/customer-360)
    * [Retail](https://www.gigaspaces.com/retail-and-ecommerce-2)
    * [Financial Services](https://www.gigaspaces.com/financial-services)
    * [Insurance Companies](https://www.gigaspaces.com/industry-solutions/insurance)
  * How it Works
    * [eRAG Technology Overview](https://www.gigaspaces.com/how-it-works/erag-how-it-works)
      * [AI-Ready, IT-Friendly](https://www.gigaspaces.com/products/ai-ready)
      * [Semantic Reasoning](https://www.gigaspaces.com/how-it-works/semantic-reasoning)
      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
  * [Success Stories](https://www.gigaspaces.com/case-studies)
    * By Use Case
      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
    * By Industry
      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
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# Model Context Protocol

[More Glossary Terms](https://www.gigaspaces.com/data-terms)

The Model Context Protocol (MCP) is an emerging standard designed to enhance how artificial intelligence (AI) systems, particularly large language models (LLMs), interact with external tools, applications, and data sources.

As AI becomes more embedded in workflows and enterprise environments, the ability to maintain consistent, interpretable, and up-to-date context across model interactions has become critical. MCP addresses this challenge by standardizing how contextual information is structured, transmitted, and utilized.

Traditionally, AI models have operated in a "stateless" manner, each user prompt is processed in isolation, limiting continuity, relevance, and collaboration across sessions. Model Context Protocol changes this by introducing a structured mechanism for managing shared state and persistent memory between models and the environments they serve.

## Key Concepts of Model Context Protocol

At its core, the Model Context Protocol provides a standardized way for developers to pass structured "context" to and from AI models. This context may include:

  * **System state** : The current application environment or session data.
  * **User profile** : Preferences, history, or roles that shape model behavior
  * **Task objectives** : Instructions or goals guiding the model’s reasoning
  * **External references** : URLs, documents, databases, and APIs used to ground model responses



By formalizing these inputs and outputs, MCP enables models to work more reliably in multi-turn conversations, multi-agent systems, and tool-augmented AI applications. Also, it supports a persistent "shared memory" layer that helps maintain context between calls to the model, which is key for workflows like document editing, coding assistance, and collaborative agents.****

##  The Components of Model Context Protocol

MCP’s architecture introduces a layered framework for orchestrating AI capabilities with greater consistency and modularity. Other key components of the Model Context Protocol architecture include:

### MCP Client

This is the interface layer (chatbot, app, or API) that sends requests to an AI model. The client is responsible for initiating the protocol, including submitting contextual metadata, defining roles, and managing interactions.

### Model Context Protocol Servers

Also called MCP servers, these act as middleware, coordinating between the AI model and its operational environment. An MCP server parses incoming context, stores relevant memory, and makes sure outputs from the model align with the task in question. It can also manage role-based access control, context filtering, and prompt engineering.

### Model Adapters

These translate both across the protocol and the capability of the underlying model. For instance, when you are interacting with different AI models (say, from OpenAI, Anthropic, or Mistral), a model adapter ensures compatibility by translating protocol structures into respective API formats.

### Context Memory Layer

This is long-term storage that stores interactions session-wise, tool-wise, and user-wise. It facilitates continuity by enabling recall from memory, collaboration history, and long-term personalization. It can be thought of as the "working memory" of an AI app.

### Context Schema

MCP mandates a formal schema, typically expressed in JSON or equivalent syntax, specifying the structure for representing contextual data. This enables it to be easier to interpret, validate, and scale across environments.

The Model Context Protocol architecture diagram usually depicts these elements as modular services communicating with each other over standardized APIs, which makes the framework extensible and interoperable.

## The Applications of Model Context Protocol

The adoption of MCP is unlocking a range of real-world model context protocol applications across consumer and enterprise domains. These include:

### Agent-based AI Systems

In multi-agent environments, where different AI models or agents collaborate on tasks, MCP facilitates coordination and memory sharing. Agents can hand off tasks, recall shared plans, and adapt to changing goals with a consistent view of context.

### Enterprise AI Workflows

For teams using AI to support business processes like customer service, document summarization, or sales assistance, MCP ensures the model remains aligned with organizational roles, tone, and business rules. It also supports traceability, making model outputs easier to audit.

### Developer Tooling & IDEs

In software development, coding assistants like Copilot or CodeWhisperer can take advantage of MCP by preserving project-specific context, shared libraries, and coding conventions between sessions, which makes them more productive and relevant.

### Collaborative Applications

Apps like Notion AI or Microsoft Loop, which embed AI for writing, brainstorming, or planning, can use MCP to manage shared workspaces where multiple users interact with the model. The protocol maintains continuity and personalization across users and sessions.

### AI Safety and Guardrails

By clearly defining what context is passed to the model and what’s returned, MCP makes it easier to enforce compliance, reduce [hallucinations](https://www.gigaspaces.com/data-terms/llm-hallucinations), and implement ethical boundaries. Developers can build stronger safeguards and [monitoring tools](https://www.gigaspaces.com/data-terms/llm-monitoring) around model interactions.

## Why Model Context Protocol Matters for AI

As generative AI moves from novelty to necessity, the need for structured, interpretable, and persistent context is more urgent than ever. The Model Context Protocol that AI developers rely on provides a foundation for reliable, scalable, and trustworthy model integration. Whether you’re building single-agent tools or complex multi-agent ecosystems, MCP delivers a framework that brings structure to creativity.

Not only does it make models smarter, it makes them situationally aware, collaborative, and accountable. 

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