MCP - Extend and Connect

Model Context Protocol

MCP (Model Context Protocol) is an open standard for connecting AI models to external data sources and tools. We build MCP-based systems that give AI models secure, structured access to your business systems.

What is MCP?

MCP provides a standardized way to:

• Connect: Link AI models to databases, APIs, and services
• Discover: Let models understand available capabilities dynamically
• Execute: Safely run tools with proper authorization and validation
• Context: Provide relevant information to models when needed

MCP Architecture

flowchart LR
    subgraph Host["AI Host"]
        A[Claude Desktop]
        B[Custom App]
        C[IDE Extension]
    end

    subgraph MCP["MCP Layer"]
        D[MCP Client]
        E[Protocol Handler]
        F[Security Layer]
    end

    subgraph Servers["MCP Servers"]
        G[Database Server]
        H[API Gateway]
        I[File System]
        J[Custom Tools]
    end

    subgraph Resources["Resources"]
        K[(PostgreSQL)]
        L[REST APIs]
        M[Documents]
        N[Services]
    end

    A & B & C --> D
    D --> E --> F
    F --> G & H & I & J
    G --> K
    H --> L
    I --> M
    J --> N

    style Host fill:#e0f2fe,stroke:#0284c7
    style MCP fill:#fef3c7,stroke:#d97706
    style Servers fill:#dcfce7,stroke:#16a34a
    style Resources fill:#f3e8ff,stroke:#9333ea

    

Our MCP Services

Custom MCP Server Development

We build MCP servers that expose your systems to AI:

Tool Development

Creating AI-accessible tools with proper interfaces:

• Business Process Automation: Order processing, approvals, notifications
• Data Retrieval: Complex queries across multiple sources
• Calculations: Financial modeling, analytics, transformations
• External API Orchestration: Multi-service coordination

Security & Governance

Integration Patterns

Technology Stack

Use Cases

For Customer Service

• Agents that can look up orders, accounts, and history
• Automated ticket routing and resolution
• Real-time inventory and availability checks

For Developers

• AI assistants with codebase access
• Automated code review and documentation
• Database query generation and execution

For Analytics

• Natural language queries against data warehouses
• Automated report generation
• Cross-system data aggregation

For Enterprise Search

• AI with access to all company documents
• Federated search across systems
• Context-aware information retrieval

Implementation Process

1. Discovery: Identify systems and data to expose to AI
2. Design: Architecture for MCP servers and security model
3. Development: Build MCP servers with proper tooling
4. Testing: Validate functionality, security, and performance
5. Deployment: Roll out with monitoring and logging
6. Iteration: Add capabilities based on usage patterns

Ready to connect AI to your systems? Get started with MCP implementation.