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.
In the context of AI-powered automation, context refers to more than just the immediate prompt. It includes:
Maintaining this context is crucial for providing accurate, consistent answers over multiple interactions.
Model Context Protocol (MCP) is a structured framework that governs how LLMs:
contextual information across multiple queries, data retrievals, and function calls.
Conceptually:
MCP = {C, U, F, S}
Where:
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:
❌ Semantic drift:
❌ Lack of live data integration:
In asset management, these issues can cause critical errors when referencing depreciation schedules or equipment assignments.
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:
❌ No context layering:
❌ No memory management:
MCP combines the advantages of retrieval and function calling while addressing their weaknesses:
✅ Context Layering:
✅ Integrated Function Results:
✅ Session Memory:
✅ Summarization and Persistence:
MCP organizes input into discrete layers:
This architecture prevents confusion and ensures each layer is processed appropriately.
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."
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:
In an enterprise asset management platform (e.g., Sellease), MCP interacts with these data layers:
MCP coordinates calls to these layers and harmonizes results within a single session.
Step 1 – User query:
“Show me the assets returned last month and the depreciation.”
Step 2 – MCP orchestrates:
Step 3 – Model response:
“Five laptops were returned last month. Depreciation totaled $2,500.”
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
Implementing MCP requires addressing several aspects:
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:
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.
