Frequently Asked Questions (FAQ) about the Gemini Image/Video Analysis MCP Server

Q: What is an MCP Server? A: MCP stands for Model Context Protocol. An MCP server acts as a bridge, allowing AI models (like those used by AI Agents) to access and interact with external data sources and tools, providing them with the context needed to perform tasks effectively.

Q: What does this Image/Video Analysis MCP Server do? A: This server allows AI Agents to analyze the content of images and videos using the Gemini 2.0 Flash model. It can analyze content from URLs, local files, and even YouTube videos.

Q: What types of content can this server analyze? A: It can analyze images and videos from URLs, local file paths, and YouTube URLs. Supported video MIME types include video/mp4, video/mpeg, video/mov, video/avi, video/x-flv, video/mpg, video/webm, video/wmv, and video/3gpp.

Q: How do I install this MCP Server? A: You can install it either via Smithery (using the provided npx command) or manually by cloning the repository, installing dependencies, and compiling the TypeScript code.

Q: Do I need an API key to use this server? A: Yes, you need a Gemini API key. You must set the GEMINI_API_KEY environment variable with your key.

Q: How do I configure this server to work with Cline or the Claude Desktop App? A: You need to add the MCP server configuration details to your cline_mcp_settings.json (for Cline) or claude_desktop_config.json (for Claude Desktop App) file. The provided example configurations show the necessary settings, including the command to run the server and the GEMINI_API_KEY environment variable.

Q: What tools are available after the server is configured? A: The following tools are available: * analyze_image: Analyzes image URLs. * analyze_image_from_path: Analyzes local image file paths. * analyze_video: Analyzes video URLs. * analyze_video_from_path: Analyzes local video file paths. * analyze_youtube_video: Analyzes a single YouTube video URL.

Q: How do I use these tools? A: Each tool takes specific arguments, such as image/video URLs or file paths, and an optional prompt. The examples in the documentation demonstrate how to call these tools with appropriate arguments.

Q: What are the limitations regarding video size? A: For videos provided via URL or path, there are size limitations (typically around 20MB after Base64 encoding). Larger videos may fail. YouTube analysis does not have this client-side download limit.

Q: What about local file paths? Are there any considerations? A: Yes, when using the ..._from_path tools, the AI assistant must specify valid file paths in the environment where the server is running. Path conversion (e.g., from Windows to WSL paths or vice versa) is the responsibility of the AI assistant or its execution environment.

Q: I see a type error during the build process. Does it affect the server’s execution? A: The TS7016 error about missing TypeScript type definitions for the mime-types module is a type checking error and does not affect the server’s execution. You can resolve it by installing the type definition file as a development dependency.

Q: Where can I find more information about UBOS and its capabilities? A: Visit the UBOS website at https://ubos.tech to learn more about the platform and its features for AI Agent development.