Model Context Protocol (MCP) and Its Integration

2025-07-06
tony

Why It Outperforms RAG and Basic Function Calling

1. Introduction

As large language models (LLMs) such as GPT-4o become embedded into enterprise information systems, organizations are experiencing a paradigm shift in how they retrieve, process, and utilize data.
Previously, combining retrieval-augmented generation (RAG) pipelines with function calling APIs was considered sufficient for enabling advanced question-answering capabilities.

However, practical asset management and finance workflows demand more than isolated retrieval or single-function queries: they require long-term context continuity, real-time state tracking, and the ability to seamlessly integrate multiple data sources.
This is where the Model Context Protocol (MCP) emerges as a fundamentally different approach.

This article explains the concept of MCP, its technical architecture, why it surpasses RAG and standalone function calling, and how it can be applied to asset management use cases.

2. Defining Model Context Protocol

2.1 What Is “Context”?

In the context of AI-powered automation, context refers to more than just the immediate prompt. It includes:

  • Conversation history and prior user queries
  • Real-time system state and data snapshots
  • User roles, permissions, and session metadata
  • The purpose of each question and how it connects to prior tasks

Maintaining this context is crucial for providing accurate, consistent answers over multiple interactions.

2.2 What Is MCP?

Model Context Protocol (MCP) is a structured framework that governs how LLMs:

  • Capture
  • Layer
  • Preserve
  • Update

contextual information across multiple queries, data retrievals, and function calls.

Conceptually:

MCP = {C, U, F, S}

Where:

  • C (Context Layers): Hierarchical segmentation of prompts, history, and results
  • U (User State): Session and user identity management
  • F (Function Calls): External data retrieval and integration
  • S (Session Memory): Token optimization and summarization

3. Comparing MCP with RAG and Function Calling

3.1 The Limitations of RAG

Retrieval-Augmented Generation (RAG) combines vector similarity search with generative language models.
While this approach has benefits—such as enabling models to answer questions from custom document sets—it also exhibits structural weaknesses in enterprise settings:

No state continuity:

  • RAG does not inherently track prior conversation turns.

Semantic drift:

  • Similarity search often retrieves content that looks relevant but contains incorrect or outdated information.

Lack of live data integration:

  • RAG cannot connect to real-time ERP systems or live databases.

In asset management, these issues can cause critical errors when referencing depreciation schedules or equipment assignments.

3.2 The Limitations of Standalone Function Calling

Function calling, as supported by GPT APIs, allows LLMs to retrieve structured data through external APIs.
However, it too has notable constraints:

Single-shot calls only:

  • It typically handles one function per prompt without automatically merging results.

No context layering:

  • There is no built-in mechanism to relate function outputs to prior conversation state.

No memory management:

  • Long conversations quickly exhaust token budgets, and results are not summarized or persisted.

3.3 Why MCP Is Superior

MCP combines the advantages of retrieval and function calling while addressing their weaknesses:

Context Layering:

  • Separates conversation history, user instructions, system outputs, and model responses.

Integrated Function Results:

  • Merges multiple function call outputs into coherent, purpose-driven answers.

Session Memory:

  • Maintains long-running conversation state over time.

Summarization and Persistence:

  • Selectively retains critical information while optimizing token usage within GPT-4o’s 128k token limit.

4. Technical Architecture of MCP

4.1 Context Layering

MCP organizes input into discrete layers:

  • System Prompt: Role definition and behavior constraints
  • User Prompt: The current query
  • Conversation History: Prior exchanges
  • Function Results: Real-time data retrieved via API

This architecture prevents confusion and ensures each layer is processed appropriately.

4.2 Multi-Step Function Calling

Unlike simple function calling, MCP allows chaining and aggregation of multiple calls:

User: "Show me last quarter's depreciation and unused assets."
MCP:
  → Call 1: /depreciation?date=last_quarter
  → Call 2: /assets/unused
Model response:
"Last quarter's total depreciation was $120,000. Currently, there are 12 unused assets."

4.3 Session Memory and Token Optimization

GPT-4o supports context windows of up to 128k tokens—one of the longest among LLMs.
Nonetheless, token limits remain a constraint for multi-session workflows.

MCP addresses this by:

  • Summarization: Condensing older exchanges into concise statements.
  • Selective Persistence: Keeping critical identifiers (asset IDs, user names) intact.
  • Layered Token Management: Assigning quotas to each context layer.

5. Integration with Enterprise Asset Management

5.1 Data Layers in Asset Management

In an enterprise asset management platform (e.g., Sellease), MCP interacts with these data layers:

  • Asset master records (ERP)
  • Licensing modules
  • Usage logs
  • Depreciation calculation services

MCP coordinates calls to these layers and harmonizes results within a single session.

5.2 Example Scenario

Step 1 – User query:
“Show me the assets returned last month and the depreciation.”

Step 2 – MCP orchestrates:

  • Retrieves conversation state
  • Identifies necessary function calls:
    • /assets/returned?date=last_month
    • /depreciation?date=last_month

Step 3 – Model response:
“Five laptops were returned last month. Depreciation totaled $2,500.”

6. Expected Benefits

Adopting MCP yields clear advantages:

✅ Consistent, context-aware responses
✅ Real-time data integration across systems
✅ Automatic report generation
✅ Superior reliability compared to RAG or function calling alone

7. Considerations and Limitations

Implementing MCP requires addressing several aspects:

  • Token Management: Summarization and pruning for long sessions
  • Latency Control: Coordinating multiple API calls efficiently
  • Security: Role-based access and data masking
  • Response Standardization: Ensuring predictable output formats

8. Conclusion

Model Context Protocol (MCP) bridges the gap between simple retrieval pipelines and truly intelligent enterprise agents.
Where RAG can produce hallucinations and function calling alone is too fragmented, MCP provides:

  • Hierarchical context management
  • Session memory
  • Integrated data retrieval
  • Predictable, auditable outputs

Sellease integrates MCP to unlock the full potential of LLM-powered asset management, delivering consistent, accurate, and automated workflows.

As enterprise AI evolves, MCP is set to become a foundational framework across asset management, finance, procurement, and compliance domains.

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