https://github.com/video-db/agent-toolkit
An open-source agent toolkit that auto-syncs SDK versions, docs, and examples—built for seamless integration with LLMs, and AI agents ( MCP compatible).
https://github.com/video-db/agent-toolkit
agent llm llms-txt mcp mcpserver videodb
Last synced: 5 months ago
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An open-source agent toolkit that auto-syncs SDK versions, docs, and examples—built for seamless integration with LLMs, and AI agents ( MCP compatible).
- Host: GitHub
- URL: https://github.com/video-db/agent-toolkit
- Owner: video-db
- Created: 2025-03-19T08:30:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-01-22T14:35:49.000Z (5 months ago)
- Last Synced: 2026-01-23T07:27:55.519Z (5 months ago)
- Topics: agent, llm, llms-txt, mcp, mcpserver, videodb
- Language: Python
- Homepage: https://docs.videodb.io
- Size: 3.02 MB
- Stars: 45
- Watchers: 0
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-mcp - video-db/agent-toolkit
- metorial-index - VideoDB Director - Integrate video database management capabilities with agents by connecting to the VideoDB Director MCP server. Manage video-related tools and resources efficiently through a seamless connection in MCP clients. (Content Creation)
- awesome-openclaw-skills - VideoDB - driven video editing, semantic search, multilingual transcription, generative... | - | (Code & Developer Tools)
README
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VideoDB Agent Toolkit
AI Agent toolkit for VideoDB
llms.txt >>
llms-full.txt
MCP
# VideoDB Agent Toolkit
The VideoDB Agent Toolkit exposes VideoDB context to LLMs and agents. It enables integration to AI-driven IDEs like Cursor, chat agents like Claude Code etc. This toolkit automates context generation, maintenance, and discoverability. It auto-syncs SDK versions, docs, and examples and is distributed through MCP and `llms.txt`
## 🚀 Quick Overview
The toolkit offers context files designed for use with LLMs, structured around key components:
`llms-full.txt` — Comprehensive context for deep integration.
`llms.txt` — Lightweight metadata for quick discovery.
`MCP (Model Context Protocol)` — A standardized protocol.
These components leverage automated workflows to ensure your AI applications always operate with accurate, up-to-date context.
## 📦 Toolkit Components
### 1. llms-full.txt ([View »](https://videodb.io/llms-full.txt))
---
`llms-full.txt` consolidates everything your LLM agent needs, including:
- Comprehensive VideoDB overview.
- Complete SDK usage instructions and documentation.
- Detailed integration examples and best practices.
**Real-world Examples:**
- [VideoDB's Director](https://chat.videodb.io) `code-assistant` agent ([View Implementation ](https://github.com/video-db/Director/blob/main/backend/director/agents/code_assitant.py))
- [VideoDB's Discord Bot](https://discord.com/invite/py9P639jGz) to power customer support and community help ([View Implementation ]())
- Integrate `llms-full.txt` directly into your LLM-powered workflows, agent systems, or AI coding environments.
### 2. llms.txt ([View »](https://videodb.io/llms.txt))
---
A streamlined file following the [Answer.AI llms.txt proposal](https://github.com/answerdotai/llms-txt). Ideal for quick metadata exposure and LLM discovery.
> **ℹ️ Recommendation**: Use `llms.txt` for lightweight discovery and metadata integration. Use `llms-full.txt` for complete functionality.
### 3. MCP (Model Context Protocol)
The VideoDB MCP Server connects with the Director backend framework, providing a single tool for many workflows. For development, it can be installed and used via uvx for isolated environments. For more details on MCPs, please visit [here](https://docs.videodb.io/add-videodb-mcp-server-in-clients-108)
**Install `uv`**
We need to install uv first.
For macOS/Linux:
```
curl -LsSf https://astral.sh/uv/install.sh | sh
```
For Windows:
```
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```
You can also visit the installation steps of `uv` for more details [here](https://docs.astral.sh/uv/getting-started/installation)
**Run the MCP Server**
You can run the MCP server using `uvx` using the following command
```
uvx videodb-director-mcp --api-key=VIDEODB_API_KEY
```
**Update VideoDB Director MCP package**
To ensure you're using the latest version of the MCP server with `uvx`, start by clearing the cache:
```
uv cache clean
```
This command removes any outdated cached packages of `videodb-director-mcp`, allowing `uvx` to fetch the most recent version.
If you always want to use the latest version of the MCP server, update your command as follows:
```
uvx videodb-director-mcp@latest --api-key=
```
## 🧠 Anatomy of LLM Context Files
LLM context files in VideoDB are modular, automatically generated, and continuously updated from multiple sources:
### 🧩 Modular Structure:
- **Instructions** — Best practices and prompt guidelines [View »](https://github.com/video-db/agent-toolkit/blob/main/context/instructions/prompt.md)
- **SDK Context** — SDK structure, classes, and interface definitions [View »](https://github.com/video-db/agent-toolkit/blob/main/context/sdk/context/index.md)
- **Docs Context** — Summarized product documentation [View »](https://github.com/video-db/agent-toolkit/blob/main/context/docs/docs_context.md)
- **Examples Context** — Real-world notebook examples [View »](https://github.com/video-db/agent-toolkit/blob/main/context/examples/examples_context.md)
### Automated Maintenance:
- Managed through GitHub Actions for automated updates.
- Triggered by changes to SDK repositories, documentation, or examples.
- Maintained centrally via a [`config.yaml`](https://github.com/video-db/agent-toolkit/blob/readme-refactor/config.yaml) file.
---
## 🛠️ Automation with GitHub Actions
Automatic context generation ensures your applications always have the latest information:
### 🔹 SDK Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_sdk_context.yml))
- **Automatically generates documentation** from SDK repo updates.
- Uses [Sphinx](https://www.sphinx-doc.org/en/master/) for Python SDKs.
### 🔹 Docs Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_docs_context.yml))
- **Scrapes and summarizes documentation** using [FireCrawl](https://www.firecrawl.dev/) and LLM-powered summarization.
### 🔹 Examples Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_examples_context.yml))
- Converts and summarizes notebooks into practical context examples.
### 🔹 Master Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_master_context.yml))
- Combines all sub-components into unified `llms-full.txt`.
- Generates standards-compliant `llms.txt`.
- Updates documentation with token statistics for transparency.
---
## 🛠️ Customization via `config.yaml`
The [`config.yaml`](https://github.com/video-db/agent-toolkit/blob/readme-refactor/config.yaml) file centralizes all configurations, allowing easy customization:
- **Inclusion & Exclusion Patterns** for documentation and notebook processing
- **Custom LLM Prompts** for precise summarization tailored to each document type
- **Layout Configuration** for combining context components seamlessly
`config.yaml` > `llms_full_txt_file` defines how `llms-full.txt` is assembled:
```yaml
llms_full_txt_file:
input_files:
- name: Instructions
file_path: "context/instructions/prompt.md"
- name: SDK Context
file_path: "context/sdk/context/index.md"
- name: Docs Context
file_path: "context/docs/docs_context.md"
- name: Examples Context
file_path: "context/examples/examples_context.md"
output_files:
- name: llms_full_txt
file_path: "context/llms-full.txt"
- name: llms_full_md
file_path: "context/llms-full.md"
layout: |
{{FILE1}}
{{FILE2}}
{{FILE3}}
{{FILE4}}
```
## 💡 Best Practices for Context-Driven Development
- **Automate Context Updates:** Leverage GitHub Actions to maintain accuracy.
- **Tailored Summaries:** Use custom LLM prompts to ensure context relevance.
- **Seamless Integration:** Continuously integrate with existing LLM agents or IDEs.
By following these practices, you ensure your AI applications have reliable, relevant, and up-to-date context—critical for effective agent performance and developer productivity.
---
## 🚀 Get Started
Clone the toolkit repository and follow the setup instructions in [`config.yaml`](https://github.com/video-db/agent-toolkit/blob/readme-refactor/config.yaml) to start integrating VideoDB contexts into your LLM-powered applications today.
**Explore further:**
- [VideoDB SDK](https://github.com/video-db/videodb-python)
- [Documentation](https://docs.videodb.io)
- [Cookbook Examples](https://github.com/video-db/videodb-cookbook)
---
[token-length-shield]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/video-db/agent-toolkit/refs/heads/main/readme_shields.json&style=for-the-badge
[token-length-url]: https://github.com/video-db/agent-toolkit/blob/main/token_breakdown.png
[tag-shield]: https://img.shields.io/github/v/tag/video-db/agent-toolkit?style=for-the-badge
[tag-url]: https://github.com/video-db/agent-toolkit/tags
[stars-shield]: https://img.shields.io/github/stars/video-db/agent-toolkit.svg?style=for-the-badge
[stars-url]: https://github.com/video-db/agent-toolkit/stargazers
[issues-shield]: https://img.shields.io/github/issues/video-db/agent-toolkit.svg?style=for-the-badge
[issues-url]: https://github.com/video-db/agent-toolkit/issues