What is an MCP Server?

An MCP (Model Context Protocol) server standardizes how applications provide context to Large Language Models (LLMs), acting as a bridge for AI Agents to interact with external data sources and tools.

How does the MCP-api-service work?

The MCP-api-service acts as middleware, translating commands from AI Agents (like Claude AI) into API calls for internal systems and then formatting the results back for the AI Agent.

What are some use cases for the MCP Server?

Use cases include HR automation (managing employee inquiries), IT service management (diagnosing technical issues), supply chain optimization (monitoring stock levels), and customer service enhancement (providing personalized support).

How do I add new scenarios to the MCP Server?

New scenarios can be added by defining new endpoints in src/config.ts, creating interfaces in src/types.ts, creating or modifying a service, and updating the src/index.ts file.

What is the MCP Inspector and how does it help with debugging?

The MCP Inspector is a tool that provides a user-friendly interface for tracking requests, viewing results, examining logs, and monitoring the performance of the MCP Server, simplifying the debugging process.

What are some best practices for using the MCP Server?

Best practices include adhering to existing conventions, implementing robust error handling, logging relevant information, and validating all input parameters.

How does the UBOS platform relate to the MCP Server?

The UBOS platform is a full-stack AI Agent Development Platform that helps businesses orchestrate AI Agents, connect them with enterprise data, build custom AI Agents, and create Multi-Agent Systems, complementing the capabilities of the MCP Server.

Where can I find more information on how to add new scenarios?

Detailed information on adding new scenarios can be found in the docs/add-new-scenario.md file.