MCP Explained: The Model Context Protocol for LLM Integration

The Problem: Every Integration is Different

When you want to connect an LLM to your systems, you traditionally have two options:

1. Proprietary Plugins: ChatGPT Plugins, Claude Integrations - each system has its own APIs, authentication, and limitations
2. Custom Code: You build individual integrations for each LLM + data source combination

Both approaches are problematic:

• High development effort
• Vendor lock-in (your integration only works with one LLM)
• Security risks from inconsistent authentication standards
• Maintenance overhead when systems update

MCP: The Open Standard

The Model Context Protocol (MCP) solves this through standardization. Developed by Anthropic, but available as an open standard for all LLMs.

The analogy: MCP is like USB for AI.

• Before USB: Each device had its own connector
• With USB: One standard for all devices
• Before MCP: Each LLM integration was custom
• With MCP: One standard for all LLM-to-tool connections

How MCP Works

┌─────────────────┐     MCP Request      ┌─────────────────┐
│                 │ ──────────────────── │                 │
│   LLM Client    │                      │   MCP Server    │
│  (Claude, GPT)  │ ◄────────────────── │    (Osiris)     │
│                 │     MCP Response     │                 │
└─────────────────┘                      └─────────────────┘
                                                │
                                                │ Access to
                                                ▼
                                     ┌─────────────────────┐
                                     │   Your Systems      │
                                     │  CRM, ERP, Graph    │
                                     └─────────────────────┘

The Three Components

1. MCP Client (the LLM)

The language model that asks questions. Can be Claude, GPT, Llama, or any other MCP-compatible LLM.

2. MCP Server (Osiris)

The middleware that receives requests from the LLM, translates them into your systems, and returns answers.

3. Your Systems (CRM, ERP, Knowledge Graph)

The actual data sources that the MCP Server accesses.

Request Flow Example

1. You ask: "Why is the Mozart Street deal stalled?"
2. LLM recognizes: "I need data from the business system"
3. LLM sends MCP Request: get_deal_status(deal="Mozart Street")
4. Osiris (MCP Server):
   • Checks authorization
   • Queries knowledge graph
   • Compresses context
   • Sends MCP Response
5. LLM receives: Structured data (deal status, missing documents, timeline)
6. LLM responds: "The deal is stalled because the energy certificate has been missing for 8 days..."

Why MCP Matters

1. LLM Interchangeability

Today you use Claude. Tomorrow GPT-5 comes out and performs better. With MCP, you switch the LLM - your integration stays.

Without MCP:     With MCP:
Claude → Custom Integration A    Claude  ┐
GPT-4  → Custom Integration B    GPT-4   ├── MCP → Osiris → Your Data
Llama  → Custom Integration C    Llama   ┘

2. Controlled Data Access (GDPR)

You define exactly in the MCP Server:

• Which data the LLM can see
• Which actions it can perform
• Who has which permissions

The LLM never sees more than you allow. GDPR-compliant by design.

3. Security Through Standardization

MCP defines secure authentication and authorization. No homegrown solutions with security vulnerabilities.

4. Tooling & Ecosystem

Since MCP is an open standard, there are:

• SDKs for Python, TypeScript, etc.
• Debugging tools
• Ready-made MCP servers for common systems
• Growing community

MCP in Osiris

Osiris implements an MCP Server that makes your Knowledge Graph accessible:

┌─────────────────────────────────────────────────────────┐
│                      LLM (Claude, GPT)                   │
│                                                          │
│  "Why is the Mozart Street deal stalled?"               │
└─────────────────────────────────────────────────────────┘
                           │
                           │ MCP Request: get_deal_analysis
                           │              deal_name="Mozart Street"
                           ▼
┌─────────────────────────────────────────────────────────┐
│                    OSIRIS MCP SERVER                     │
│                                                          │
│  ✓ Check authorization                                  │
│  ✓ Execute graph query                                  │
│  ✓ Compress context                                     │
│  ✓ Structured response                                  │
└─────────────────────────────────────────────────────────┘
                           │
                           │ Graph Query (Cypher)
                           ▼
┌─────────────────────────────────────────────────────────┐
│                   NEO4J KNOWLEDGE GRAPH                  │
│                                                          │
│  [Deal] --CONCERNS--> [Property] --MISSING--> [Document]│
└─────────────────────────────────────────────────────────┘

The result: You speak to the LLM in natural language. The LLM queries Osiris via MCP. Osiris delivers deterministic facts from the graph. You get precise answers.

→ How Osiris uses MCP