Name: VideoDB Director
Author: video-db

Question 1

What is the VideoDB Agent Toolkit?

Accepted Answer

The VideoDB Agent Toolkit is an open-source tool designed to expose VideoDB context to Large Language Models (LLMs) and AI agents. It automates context generation, maintenance, and discoverability, making it easier to integrate VideoDB with AI-driven applications.

Question 2

What are the main components of the VideoDB Agent Toolkit?

Accepted Answer

The toolkit includes `llms-full.txt` (comprehensive context for deep integration), `llms.txt` (lightweight metadata for quick discovery), and MCP (Model Context Protocol), a standardized protocol for AI model interaction.

Question 3

What is MCP (Model Context Protocol)?

Accepted Answer

MCP is an open protocol that standardizes how applications provide context to LLMs. The MCP server acts as a bridge, allowing AI models to access and interact with external data sources and tools.

Question 4

How do I install the VideoDB Agent Toolkit?

Accepted Answer

First, clone the toolkit repository from GitHub. Then, install the necessary dependencies, including `uv`. Finally, run the MCP server using `uvx`.

Question 5

How do I run the MCP server?

Accepted Answer

You can run the MCP server using `uvx` with the command: `uvx videodb-director-mcp --api-key=VIDEODB_API_KEY`. Replace `VIDEODB_API_KEY` with your actual VideoDB API key.

Question 6

How do I update the VideoDB Director MCP package?

Accepted Answer

To ensure you're using the latest version, clear the cache with `uv cache clean` and then update the package with `uvx videodb-director-mcp@latest --api-key=<VIDEODB_API_KEY>`.

Question 7

What is the purpose of `llms-full.txt`?

Accepted Answer

`llms-full.txt` consolidates everything your LLM agent needs, including a comprehensive VideoDB overview, complete SDK usage instructions and documentation, and detailed integration examples and best practices.

Question 8

What is the purpose of `llms.txt`?

Accepted Answer

`llms.txt` is a streamlined file following the Answer.AI `llms.txt` proposal, ideal for quick metadata exposure and LLM discovery.

Question 9

How is the LLM context maintained and updated?

Accepted Answer

The LLM context is managed through GitHub Actions for automated updates, triggered by changes to SDK repositories, documentation, or examples. It is maintained centrally via a `config.yaml` file.

Question 10

Can I customize the toolkit?

Accepted Answer

Yes, the `config.yaml` file centralizes all configurations, allowing easy customization of inclusion/exclusion patterns, custom LLM prompts, and layout configuration.

Question 11

What are some best practices for context-driven development with this toolkit?

Accepted Answer

Automate context updates using GitHub Actions, tailor summaries with custom LLM prompts, and continuously integrate with existing LLM agents or IDEs.

Question 12

How does UBOS relate to the VideoDB Agent Toolkit?

Accepted Answer

UBOS is a full-stack AI Agent Development Platform that complements the VideoDB Agent Toolkit. While the toolkit integrates LLMs with VideoDB, UBOS orchestrates AI Agents, connects them with enterprise data, and facilitates building custom AI applications.

Question 13

Where can I find more information about VideoDB SDK, documentation, and cookbook examples?

Accepted Answer

You can explore the VideoDB SDK at [VideoDB SDK](https://github.com/video-db/videodb-python), documentation at [Documentation](https://docs.videodb.io), and cookbook examples at [Cookbook Examples](https://github.com/video-db/videodb-cookbook).

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