Question 1

What is an MCP Server?

Accepted Answer

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.

Question 2

What does this Image/Video Analysis MCP Server do?

Accepted Answer

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.

Question 3

What types of content can this server analyze?

Accepted Answer

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`.

Question 4

How do I install this MCP Server?

Accepted Answer

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.

Question 5

Do I need an API key to use this server?

Accepted Answer

Yes, you need a Gemini API key. You must set the `GEMINI_API_KEY` environment variable with your key.

Question 6

How do I configure this server to work with Cline or the Claude Desktop App?

Accepted Answer

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.

Question 7

What tools are available after the server is configured?

Accepted Answer

The following tools are available:n    *   `analyze_image`: Analyzes image URLs.n    *   `analyze_image_from_path`: Analyzes local image file paths.n    *   `analyze_video`: Analyzes video URLs.n    *   `analyze_video_from_path`: Analyzes local video file paths.n    *   `analyze_youtube_video`: Analyzes a single YouTube video URL.

Question 8

How do I use these tools?

Accepted Answer

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.

Question 9

What are the limitations regarding video size?

Accepted Answer

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.

Question 10

What about local file paths? Are there any considerations?

Accepted Answer

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.

Question 11

I see a type error during the build process. Does it affect the server's execution?

Accepted Answer

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.

Question 12

Where can I find more information about UBOS and its capabilities?

Accepted Answer

Visit the UBOS website at [https://ubos.tech](https://ubos.tech) to learn more about the platform and its features for AI Agent development.

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