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MCP Server

Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open protocol that enables controlled interactions between AI systems and agents. It enables external tools and data sources to be utilized and read by AI agents that implement the protocol.

Developed by Anthropic, MCP standardizes how AI applications can connect to a robust number of services while maintaining user control.

What's MCP?

MCP acts as a bridge between AI systems (like Cursor, Claude Desktop, ChatGPT, or other LLMs) and external resources such as:

• APIs and databases
• File systems and cloud storage
• Development tools and services
• Custom business backends and workflows

The protocol ensures that AI interactions with external systems are:

• Auditable: Full visibility into what tools are accessed and how they're used via a simple JSON-RPC 2.0 message flow.
• Standardized: Consistent interface across different tools, servers, clients, languages, and services.

How Zuplo Enables MCP

Zuplo's MCP Server Handler provides a perfect foundation for MCP implementations by:

1. Unified API Interface: Transform any backend API service into a standardized MCP-compatible server
2. Security & Control: Built-in authentication, rate limiting, and access controls
3. Monitoring & Analytics: Full observability into AI tool usage and performance
4. Developer Experience: Easy configuration and deployment using your existing OpenAPI specifications

The MCP Server Handler transforms your existing Zuplo API gateway into a powerful toolset that AI systems can discover, understand, and invoke - bringing AI capabilities directly into your business workflows!

MCP Implementation Options

Zuplo provides two approaches for implementing MCP servers:

1. MCP Server Handler: Transform Routes into AI Tools

The MCP Server Handler automatically transforms your API gateway routes into MCP tools that AI systems can discover and use.

How It Works

The MCP Server Handler:

1. Route Discovery: Automatically exposes your Zuplo routes as discoverable MCP tools
2. OpenAPI Integration: Uses your existing OpenAPI specifications to provide tool descriptions
3. Secure Access: Leverages Zuplo's authentication and authorization policies
4. Real-time Execution: AI systems can invoke your routes as tools in real-time

Example Use Cases

Customer Service AI Tools

Transform your customer management APIs into AI tools:

Code
- GET /customers/{id} → "Get customer information for user 123"
- POST /tickets → "Create a support ticket with the following ..."
- PUT /customers/{id}/status → "Update customer 123 status ..."

E-commerce AI Assistant

Expose your e-commerce APIs as shopping tools:

Code
- GET /products/search → "Search for products ..."
- POST /cart/add → "Add item to cart"
- GET /orders/{id} → "Get order status"

DevOps Automation

Make your infrastructure APIs available to AI:

Code
- GET /deployments → "List deployments"
- POST /deployments → "Create new deployment"
- GET /metrics → "Get system metrics"

Security Considerations

When exposing routes as MCP tools:

1. Apply appropriate authentication policies to ensure only authorized AI systems can access your tools
2. Use rate limiting to prevent abuse and control usage costs
3. Implement audit logging to track tool usage and maintain compliance
4. Scope permissions carefully - only expose routes and OpenAPI specs that should be accessible to AI systems

Learn More

• MCP Server Handler Technical Documentation
• MCP Tools Documentation
• MCP Prompts Documentation
• MCP Resources Documentation
• MCP Custom Tools Documentation
• Model Context Protocol Specification