What are the runtime requirements?
Python 3.9+, Ollama installed with the Gemma 3 model, and a SERP API key for YouTube search.
@arjunprabhulal
Basic information
Build AI Agent using Google ADK , MCP and Gemma 3 model
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"adk-mcp-gemma3": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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.
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.”
Python 3.9+, Ollama installed with the Gemma 3 model, and a SERP API key for YouTube search.
Create a `.env` file in the project root directory with the line `SERP_API_KEY=your_serp_api_key_here`.
Ensure your query clearly requires external information. You can also try making the tool description more explicit in the agent configuration.
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.
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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