Frequently Asked Questions about the Keka MCP Server

Q: What is the Keka MCP Server?

A: The Keka MCP Server is a Model Context Protocol (MCP) server designed to integrate with the Keka HR platform. It exposes Keka HR functionalities as MCP tools, allowing AI models to access and interact with Keka data.

Q: What is MCP (Model Context Protocol)?

A: MCP is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). It allows AI models to understand and interact with data from different sources.

Q: What Keka HR functionalities are exposed as MCP tools?

A: The server provides tools for employee management, attendance tracking, leave management, payslip retrieval, and holiday calendars.

Q: What are the benefits of using the Keka MCP Server?

A: Benefits include seamless integration with Keka, enhanced automation of HR tasks, data-driven decision-making, improved employee experience, robust security, and a modular, extensible architecture.

Q: How does the Keka MCP Server ensure security?

A: The server uses OAuth2 authentication to securely connect to Keka, ensuring only authorized access to your HR data.

Q: What environment variables are required to run the Keka MCP Server?

A: You need to set KEKA_CLIENT_ID, KEKA_CLIENT_SECRET, and KEKA_REFRESH_TOKEN.

Q: Can I deploy the Keka MCP Server using Docker?

A: Yes, the server includes a ready-to-use Dockerfile for containerized deployment.

Q: What are some use cases for the Keka MCP Server?

A: Use cases include AI-powered chatbots for HR inquiries, automated onboarding processes, personalized learning and development programs, predictive analytics for employee retention, and performance management systems.

Q: How do I install the Keka MCP Server on UBOS?

A: Access the UBOS Asset Marketplace, search for the Keka MCP Server, and follow the installation instructions.

Q: What is UBOS?

A: UBOS is a full-stack AI Agent development platform focused on bringing AI Agents to every business department. It helps orchestrate AI Agents, connect them with enterprise data, build custom AI Agents with your LLM model, and create Multi-Agent Systems.