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YouTube Search Assistant with ADK, MCP and Gemma 3

@arjunprabhulal

代码仓库主页

基本信息

分类

AI 与智能体

许可证

MIT

传输方式

stdio

作者

arjunprabhulal

关于 YouTube Search Assistant with ADK, MCP and Gemma 3

Build AI Agent using Google ADK , MCP and Gemma 3 model

#mcp#google-cloud-platform#youtube-search#adk#ollama#mcp-server#mcp-client#gemma3

YouTube Search Assistant with ADK, MCP and Gemma 3 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "adk-mcp-gemma3": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

YouTube Search Assistant with ADK, MCP and Gemma 3 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is YouTube Search Assistant with ADK, MCP and Gemma 3?

A practical implementation demonstrating YouTube search functionality using Google's Agent Development Kit (ADK), Model Context Protocol (MCP), and the Gemma 3 model hosted locally via Ollama. It creates a conversational agent that can search YouTube, format results, and respond in natural language.

How to use YouTube Search Assistant with ADK, MCP and Gemma 3?

Clone the repository, install dependencies, set up a .env file with your SERP API key, and pull the Gemma 3 model with Ollama. Then run adk web for a browser-based UI or python -m search for command‑line interaction. Example queries include “Find videos about Google Cloud Next 25” or “Search for YouTube tutorials on Python programming.”

Key features of YouTube Search Assistant with ADK, MCP and Gemma 3

  • Search for YouTube videos using natural language queries
  • Powered by Gemma 3 running locally on Ollama
  • Formats search results in a clean, easy-to-read format
  • Built with Google’s Agent Development Kit (ADK)
  • Integrates Model Context Protocol (MCP) for tool communication
  • Uses SERP API to access YouTube data

Use cases of YouTube Search Assistant with ADK, MCP and Gemma 3

  • Find videos about specific events (e.g., Google Cloud Next 25)
  • Search for YouTube tutorials on programming languages
  • Look for educational videos on machine learning topics
  • Create a conversational assistant for video discovery

常见问题

1

What are the runtime requirements?

Python 3.9+, Ollama installed with the Gemma 3 model, and a SERP API key for YouTube search.

2

How do I set up the SERP API key?

Create a `.env` file in the project root directory with the line `SERP_API_KEY=your_serp_api_key_here`.

3

What if the model doesn’t use tools properly?

Ensure your query clearly requires external information. You can also try making the tool description more explicit in the agent configuration.

4

What should I do if I encounter a LiteLLM/Ollama KeyError?

This is a known bug caused by Ollama’s JSON format responses and LiteLLM’s parsing. Workarounds include manually patching LiteLLM with the changes from PR #9966 or avoiding `format=json` in requests.

5

What if I have memory constraints?

The 12B model requires significant RAM/VRAM. Consider using a smaller model like `gemma3:7b` or `gemma3:1b` if you experience memory issues.

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