Frequently Asked Questions

Q: What is an MCP Server? A: MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. An MCP server acts as a bridge, allowing AI models to access and interact with external data sources and tools.

Q: How does the Interactive Feedback MCP Server reduce costs? A: By prompting the AI to request user feedback before completing tasks, the server minimizes unnecessary tool calls, leading to significant savings, especially on platforms like Cursor.

Q: Which AI development tools are compatible with this MCP Server? A: The Interactive Feedback MCP Server is designed to work seamlessly with popular AI development tools like Cursor, Cline, and Windsurf.

Q: What are the prerequisites for installing the MCP Server? A: You need Python 3.11 or newer and uv (a Python package manager) installed.

Q: How do I configure the server in Cursor? A: You can configure it either manually via mcp.json or through Cursor’s settings, pointing it to the running server.

Q: Can I use this server for testing and debugging? A: Yes, the MCP server can be integrated into testing and debugging workflows to generate test cases, analyze code, and provide feedback on AI’s findings.

Q: Where are the configuration settings stored? A: The server uses Qt’s QSettings to store configuration on a per-project basis in platform-specific locations.

Q: How does this MCP integrate with the UBOS platform? A: It allows for finer-grained control over AI Agent behavior within UBOS, ensuring AI-driven processes align with business objectives and human guidance.

Q: What if I have questions or suggestions about the Interactive Feedback MCP Server? A: You can reach out to Fábio Ferreira on X @fabiomlferreira or check out dotcursorrules.com for more information.